Business transaction processing methods, devices, electronic equipment, storage media and program products

Abstract

This application provides a transaction processing method, apparatus, electronic device, storage medium, and program product, relating to the field of artificial intelligence technology. The method includes: obtaining a data export request; obtaining data to be processed based on the data export request; wherein the data to be processed is business data between the current user and a target object. The data to be processed is then mined using a preset data mining algorithm to determine the correlation relationships between the data. Based on the correlation relationships, business prediction is performed on the data to be processed to obtain prediction result information; wherein the prediction result information represents the completion degree of the business. Based on the prediction result information, business strategy information is generated; wherein the business strategy information represents the strategy that the current user needs to execute at the current time. This method aims to improve the accuracy and efficiency of transaction decisions.

Description

Technical Field

[0001] This application relates to the field of artificial intelligence technology, and in particular to a business transaction processing method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] Currently, transaction data, such as financial data, is a crucial component of business transaction decision-making. Therefore, further data analysis is necessary to monitor business progress and actual transaction status in real time.

[0003] In existing technologies, data is usually summarized and aggregated manually to understand the actual transaction situation of the business.

[0004] However, existing technologies may not be able to comprehensively analyze a company's financial data or effectively integrate this data into the transaction decision-making process, resulting in low accuracy and efficiency of the transaction decisions. Summary of the Invention

[0005] This application provides a business transaction processing method, apparatus, electronic device, storage medium, and program product to improve the accuracy and efficiency of transaction decisions.

[0006] In a first aspect, embodiments of this application provide a business transaction processing method, including:

[0007] Obtain a data export request, and obtain the data to be processed based on the data export request; wherein, the data to be processed is the business data between the current user and the target object;

[0008] According to the preset data mining algorithm, the data to be processed is mined to determine the correlation relationship of the data to be processed;

[0009] Based on the aforementioned correlation, business prediction is performed on the data to be processed to obtain prediction result information; wherein, the prediction result information represents the completion degree of the business.

[0010] Based on the prediction results, business strategy information for the service is generated; wherein, the business strategy information represents the strategy that the current user needs to execute at the current time.

[0011] In one possible implementation, the step of performing data mining processing on the data to be processed according to a preset data mining algorithm to determine the correlation relationship of the data to be processed includes:

[0012] Import the preset library;

[0013] Based on the preset library and preset data mining algorithm, the data to be processed is mined to obtain mining result information; wherein, the mining result information includes the values ​​of multiple preset indicators;

[0014] Based on the mining results, the data to be processed is grouped to obtain multiple groups of data, and the correlation between each group of data is determined.

[0015] In one possible implementation, the step of performing business prediction on the data to be processed based on the association relationship to obtain prediction result information includes:

[0016] Based on the correlation between the data sets, a business prediction is performed on the data to be processed using a preset prediction model to obtain the completion rate of each type of preset indicator.

[0017] Based on the completion rate of each type of indicator, predictive results are generated.

[0018] In one possible implementation, generating business strategy information based on the prediction result information includes:

[0019] Based on the prediction results, the risk level of the business is determined;

[0020] Based on the risk level, business strategy information is generated.

[0021] In one possible implementation, the method further includes:

[0022] The system monitors the data to be processed, and if it is determined that the data exceeds a preset threshold range, an early warning signal is generated.

[0023] In one possible implementation, the method further includes:

[0024] Determine the association strength level of each association, and determine the support and confidence level corresponding to the association strength level;

[0025] A heatmap or funnel plot is generated based on the association strength level, the support and confidence level corresponding to the association strength level.

[0026] Secondly, embodiments of this application provide a business transaction processing apparatus, comprising:

[0027] The acquisition module is used to acquire data export requests and acquire data to be processed based on the data export requests; wherein, the data to be processed is the business data between the current user and the target object;

[0028] The data mining module is used to perform data mining processing on the data to be processed according to a preset data mining algorithm to determine the correlation relationship of the data to be processed.

[0029] The prediction module is used to perform business prediction on the data to be processed based on the correlation relationship, and obtain prediction result information; wherein, the prediction result information represents the completion degree of the business.

[0030] The generation module is used to generate business strategy information for the service based on the prediction result information; wherein the business strategy information represents the strategy that the current user needs to execute at the current time.

[0031] In one possible implementation, the mining module is specifically used for:

[0032] Import the preset library;

[0033] Based on the preset library and preset data mining algorithm, the data to be processed is mined to obtain mining result information; wherein, the mining result information includes the values ​​of multiple preset indicators;

[0034] Based on the mining results, the data to be processed is grouped to obtain multiple groups of data, and the correlation between each group of data is determined.

[0035] In one possible implementation, the prediction module is specifically used for:

[0036] Based on the correlation between the data sets, a business prediction is performed on the data to be processed using a preset prediction model to obtain the completion rate of each type of preset indicator.

[0037] Based on the completion rate of each type of indicator, predictive results are generated.

[0038] In one possible implementation, the generation module is specifically used for:

[0039] Based on the prediction results, the risk level of the business is determined;

[0040] Based on the risk level, business strategy information is generated.

[0041] In one possible implementation, the device is further specifically used for:

[0042] The system monitors the data to be processed, and if it is determined that the data exceeds a preset threshold range, an early warning signal is generated.

[0043] In one possible implementation, the device is further specifically used for:

[0044] Determine the association strength level of each association, and determine the support and confidence level corresponding to the association strength level;

[0045] A heatmap or funnel plot is generated based on the association strength level, the support and confidence level corresponding to the association strength level.

[0046] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0047] The memory stores computer-executed instructions;

[0048] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0049] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0050] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0051] The transaction processing method, apparatus, electronic device, storage medium, and program product provided in this application embodiment obtain a data export request and, based on the data export request, obtain data to be processed; wherein, the data to be processed is the business data between the current user and the target object. According to a preset data mining algorithm, the data to be processed is mined to determine the correlation relationships of the data to be processed. Based on the correlation relationships, business prediction is performed on the data to be processed to obtain prediction result information; wherein, the prediction result information represents the completion degree of the business. Based on the prediction result information, business strategy information is generated; wherein, the business strategy information represents the strategy that the current user needs to execute at the current time. In this solution, by performing in-depth mining processing on the data to be processed, the correlation relationships of the data to be processed are obtained, and based on the correlation relationships, business prediction is performed on the data to be processed to obtain prediction result information, thereby generating business strategy information. Therefore, through comprehensive and accurate analysis and in-depth mining of the data to be processed, key information is effectively extracted from massive amounts of data, and business strategy information is generated, thereby providing comprehensive and accurate information support for enterprise transaction decisions, thereby improving the accuracy and efficiency of transaction decisions. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0053] Figure 1 A flowchart illustrating a business transaction processing method provided in this application embodiment. Figure 1 ;

[0054] Figure 2 A flowchart illustrating another business transaction processing method provided in this application embodiment. Figure 2 ;

[0055] Figure 3 A schematic diagram of the structure of a business transaction processing device provided in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0057] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0058] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0059] Currently, transaction data, such as financial data, is a crucial component of business transaction decision-making. Therefore, further data analysis is necessary to monitor business progress and actual transaction status in real time.

[0060] In one example, data is typically summarized and aggregated manually to understand the actual transaction situation of the business. However, existing technologies may not be able to comprehensively analyze a company's financial data or effectively integrate this data into the transaction decision-making process, resulting in low accuracy and efficiency of the transaction decisions.

[0061] Based on the above scenarios, it is clear that existing technologies suffer from low accuracy and efficiency in transaction decision-making.

[0062] The transaction processing method provided in this application effectively extracts key information from massive amounts of data and generates business strategy information through comprehensive, accurate analysis and in-depth mining of the data to be processed. This provides comprehensive and accurate information support for enterprise transaction decisions and solves the technical problem of low accuracy and efficiency in transaction decisions.

[0063] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0064] Figure 1 A flowchart illustrating a business transaction processing method provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0065] S101. Obtain the data export request and obtain the data to be processed based on the data export request; wherein, the data to be processed is the business data between the current user and the target object.

[0066] For example, the executing entity of this embodiment can be an electronic device, a terminal device, a business transaction processing device or equipment, or other device or equipment capable of executing this embodiment, and there is no limitation thereto. In this embodiment, the executing entity is described as an electronic device.

[0067] First, there is a business relationship between the current user and the target, and the data generated by the business is the data to be processed. For example, the data to be processed includes key data such as total contract amount, average contract amount, transaction frequency, contract completion rate, and payment status statistics, without any limitation. The current user and the target can be an individual user or a corporate entity of a certain size.

[0068] In this step, a data export request is obtained, and based on the request, the business-generated data to be processed is exported from a pre-defined database. The data to be processed then undergoes data cleaning, data transformation, data integration, and data loading operations to obtain the final data to be processed. Data cleaning includes deleting duplicate data, handling missing values, and cleaning outliers. At this stage, at least one pre-defined function in Pandas and some conditional statements are needed to handle outliers, thereby ensuring the accuracy and integrity of the data. Data transformation converts the data into a unified format and standard. This may involve data type conversion, label encoding, and possible normalization. For example, Pandas' pre-defined conversion functions can be used to convert data types to a unified format, or pre-defined encoding functions can be used for label encoding. Data integration combines multiple cleaned and transformed data sets using big data technologies, enabling the processing of massive amounts of data and improving data processing efficiency. Data loading loads the cleaned and transformed data into a database or data warehouse for subsequent analysis and decision support. At this stage, SQL statements or Pandas' pre-defined loading functions can be used to load the data into the database.

[0069] S102. Based on the preset data mining algorithm, perform data mining processing to determine the correlation between the data to be processed.

[0070] For example, in the exploratory data analysis phase, a pre-defined data mining algorithm and an imported Pandas library are used to perform various statistical and in-depth processing on the integrated data to be processed using association rule mining. This reveals transaction patterns and potential relationships between enterprises, thereby obtaining the associations within the data. The pre-defined data mining algorithm can be a Python algorithm, without limitation; the algorithm can be association rule mining, cluster analysis, or other similar data mining algorithms. Furthermore, artificial intelligence (AI) technology can be used to perform trend analysis on the data to be processed, improving accuracy.

[0071] Therefore, by conducting in-depth mining and processing of the integrated data, the transaction relationships and potential patterns between current users and target objects can be uncovered, which will facilitate data support for enterprise transaction decisions in the next step.

[0072] S103. Based on the correlation, perform business prediction on the data to be processed to obtain prediction result information; wherein, the prediction result information represents the degree of completion of the business.

[0073] For example, based on the correlation, business forecasting is performed on the data to be processed to determine the risk level of the business, and based on the risk level, forecasting result information is obtained, which represents the degree of completion of the business.

[0074] S104. Based on the prediction results, generate business strategy information for the business; wherein, the business strategy information represents the strategy that the current user needs to execute at the current time.

[0075] For example, electronic devices can generate business strategy information based on the assessed risk level. This information serves as a suggestion for adjusting transaction strategies that the user needs to execute at the current time, thus providing specific support and advice for the company's transaction decisions. Specifically, parameters such as contract completion rate, delivery or payment status, and satisfaction levels are collected to form trend analysis conclusions, assess the transaction risk and stability with target entities, and provide corresponding transaction strategy adjustment suggestions. For example, it might suggest increasing the flexibility of contract terms or strengthening communication with core enterprises to reduce risk. Decision-makers then make the final transaction decision based on these suggestions. Furthermore, through in-depth analysis and processing of the data, flexible transaction strategy adjustments and business optimization suggestions can be provided to the company based on forecast results. Moreover, for data such as correlation relationships, forecast results, and business strategy information, blockchain technology is used for trusted storage during generation to ensure the authenticity, validity, and integrity of the data.

[0076] The transaction processing method provided in this application involves obtaining a data export request and acquiring data to be processed based on the request. The data to be processed is the business data between the current user and the target object. A preset data mining algorithm is used to mine and process the data to determine the relationships between the data. Based on these relationships, business prediction is performed on the data to obtain prediction results, which represent the completion level of the business. Based on the prediction results, business strategy information is generated, representing the strategy that the current user needs to execute. In this solution, by performing deep mining on the data to be processed, the relationships between the data are obtained. Based on these relationships, business prediction is performed on the data to be processed, and prediction results are obtained, thereby generating business strategy information. Therefore, through comprehensive and accurate analysis and deep mining of the data to be processed, key information is effectively extracted from massive amounts of data, and business strategy information is generated, providing comprehensive and accurate information support for enterprise transaction decisions, thereby improving the accuracy and efficiency of transaction decisions.

[0077] Figure 2 A flowchart illustrating a business transaction processing method provided in this application. Figure 2 ,like Figure 2As shown, in this embodiment... Figure 1 Based on the embodiments, the transaction processing method for the business is described in detail, and the method includes:

[0078] S201. Obtain the data export request and obtain the data to be processed based on the data export request; wherein, the data to be processed is the business data between the current user and the target object.

[0079] For example, this step can be referred to Figure 1 Step 101 in the text will not be repeated here.

[0080] S202, Import the preset library.

[0081] For example, electronic devices can import Pandas (for data processing and analysis), Matplotlib (for data visualization), and Seaborn (a high-level visualization library based on Matplotlib).

[0082] S203. Based on the preset library and preset data mining algorithm, perform mining processing on the data to be processed to obtain mining result information; wherein, the mining result information includes the values ​​of multiple preset indicators.

[0083] For example, an electronic device can perform data mining on the data to be processed according to the preset `describe` method and preset data mining algorithm in the Pandas library, and obtain the values ​​of various preset indicators of the data to be processed, i.e., the mining result information. For example, preset indicators include mean, median, standard deviation, etc., and there is no limitation on them.

[0084] S204. Based on the mining results, the data to be processed is grouped to obtain multiple groups of data, and the correlation between each group of data is determined.

[0085] For example, based on the data mining results, the electronic device can use Pandas' groupby method to group the data to be processed and use the apply method to perform calculations on each group. Then, these calculation results are used to generate associations between the groups of data.

[0086] S205. Based on the correlation between the data in each group, a business prediction is made on the data to be processed using a preset prediction model to obtain the completion rate of each type of preset indicator.

[0087] For example, machine learning algorithms can be used to build predictive models. Specifically, first, the dataset is loaded; the features in the dataset used for model training are determined; the features most relevant to the target variable (such as contract completion rate, etc.) are selected; data preprocessing is performed, such as handling missing values, outliers, and duplicate values; some features may need to be transformed, such as normalization or standardization; and the data is divided into training and test sets. Then, a model is selected, which can be a linear regression model, or, depending on the characteristics of the data, a decision tree, random forest, vector machine, or neural network, depending on the characteristics of the data and the complexity of the problem. The selected model is trained on the training set, and the model is used for prediction, evaluation, result analysis, and optimization on the test set until the model converges, resulting in the trained predictive model. For example, the predictive model could be a linear regression model.

[0088] In this step, the electronic device can use a linear regression model to make business predictions based on the correlation between different sets of data, obtaining the completion rates of various preset indicators. These indicators include key metrics such as contract completion rate and payment status, which can predict the contract completion rate between the current company and the target company.

[0089] S206. Generate prediction results information based on the completion rate of each type of indicator.

[0090] For example, an electronic device can generate a comprehensive prediction result based on the completion rate of various types of indicators. Specifically, the average completion rate of each type of indicator can be calculated, and the prediction result information can be determined based on the average value. The prediction result information represents the degree of completion of the business. For example, the average value can be compared with multiple preset completion value ranges, each completion value range corresponding to a completion degree, including incomplete, basically completed, fully completed, etc. If it is determined that the average value falls within any completion value range, then the completion degree of the business is determined to be the completion degree corresponding to the completion value range that falls within it.

[0091] S207. Determine the risk level of the business based on the forecast results.

[0092] For example, electronic devices can determine the risk level of a business based on forecast information. For instance, if the forecast information predicts a low contract completion rate, it may indicate a high level of transaction risk.

[0093] S208. Generate business strategy information based on the risk level.

[0094] For example, electronic devices can generate business strategy information based on the assessed risk level; this information serves as a recommendation for adjusting transaction strategies. For instance, it might suggest increasing the flexibility of contract terms or strengthening communication with core enterprises to mitigate risk. Decision-makers then make the final transaction decision based on these recommendations.

[0095] S209. Monitor the data to be processed. If it is determined that the data to be processed exceeds the preset threshold range, generate an early warning signal.

[0096] For example, electronic devices can monitor data to be processed in real time. If the data exceeds a preset threshold, an early warning signal is issued promptly. For instance, if a delay in payment or a decrease in contract completion rate is detected between a current user and a subsidiary of a company, other subsidiaries that have signed contracts with the current user can be promptly notified, providing the company with risk prevention and solutions. Therefore, the ability to monitor a company's business transaction data in real time can promptly detect anomalies and provide early warnings, helping companies implement risk prevention and response measures, thereby improving the security and reliability of transactions.

[0097] S210. Determine the association strength level of each association, and determine the support and confidence level corresponding to the association strength level.

[0098] For example, there are multiple association strength levels for each association relationship. The electronic device can determine the association strength level of each association relationship and calculate the support and confidence levels corresponding to the association strength levels.

[0099] S211. Generate a heatmap or funnel plot based on the association strength level, the support and confidence level corresponding to the association strength level.

[0100] For example, electronic devices can use Python and libraries such as Pandas and Seaborn to draw heatmaps or create funnel plots to represent the differences in support and confidence between different levels of association strength. They can also draw bar charts, box plots, etc. to explore the distribution and correlation of the data to be processed, and then present the prediction results to users in the form of charts, graphs, or visualizations, realizing data visualization. Users can understand the data situation of the business more clearly and intuitively, which facilitates more informed judgments on transaction decisions.

[0101] The transaction processing method for a business provided in this application embodiment involves: obtaining a data export request; obtaining data to be processed based on the data export request; wherein the data to be processed is the business data between the current user and the target object; importing a preset library; performing data mining processing on the data to be processed based on the preset library and a preset data mining algorithm to obtain mining result information; wherein the mining result information includes the values ​​of multiple preset indicators; grouping the data to be processed based on the mining result information to obtain multiple groups of data, and determining the correlation between each group of data; based on the correlation between each group of data, performing business prediction on the data to be processed using a preset prediction model to obtain the completion rate of preset indicators of each type; generating prediction result information based on the completion rate of each type of indicator; determining the risk level of the business based on the prediction result information; generating business strategy information based on the risk level; monitoring the data to be processed, and generating an early warning signal if it is determined that the data to be processed exceeds a preset threshold range; determining the correlation strength level of each correlation, and determining the support and confidence level corresponding to the correlation strength level; generating a heatmap or funnel chart based on the correlation strength level, the support and confidence level corresponding to the correlation strength level. Therefore, by conducting comprehensive and accurate analysis and in-depth mining of the data being processed, key information can be effectively extracted from massive amounts of data, and business strategy information can be generated, thereby providing comprehensive and accurate information support for the company's transaction decisions, so as to improve the accuracy and efficiency of transaction decisions.

[0102] For example, this application provides an architecture for a business transaction processing method, including a database, a data integration module, a data analysis module, a transaction decision support module, a data visualization module, and a real-time monitoring and early warning module. The database contains various types of data to be processed, including financial data such as total contract amount, average contract amount, transaction frequency, contract completion rate, and payment statistics. The data integration module integrates the data exported from the database, providing foundational data for subsequent analysis. The data analysis module uses data mining algorithms (such as association rule mining and cluster analysis) to perform in-depth analysis of the integrated data, discovering transaction patterns and potential relationships between enterprises. The transaction decision support module provides transaction decision suggestions to enterprises based on the analysis results, including adjustments to transaction strategies and business optimization. The data visualization module displays the analysis results to users in the form of charts or graphs, allowing users to intuitively understand the enterprise's financial situation and transaction relationships. The real-time monitoring and early warning module monitors the enterprise's transaction data in real time, detects anomalies, and issues timely warnings, providing enterprises with risk prevention and response measures.

[0103] The workflow between the above modules includes: Data Input, which involves exporting data to be processed between two companies from a pre-set database; Data Integration, which involves integrating the exported data to form a unified data format and standard; Data Analysis, which involves analyzing the integrated data and using data mining algorithms to uncover transaction patterns and potential relationships between companies to obtain correlations; Transaction Decision Support, which involves generating business strategy information based on the prediction results and providing companies with transaction decision suggestions, including adjustments to transaction strategies and business optimization; Data Visualization, which involves displaying the prediction results to users in the form of charts or graphs; and Real-time Monitoring and Early Warning, which involves real-time monitoring of the company's transaction data, detecting anomalies, and issuing timely warnings.

[0104] Therefore, this application has the following effects: 1. Improved accuracy of transaction decisions: Through comprehensive and in-depth financial data analysis and integration, it helps enterprises to more accurately understand key financial data related to core enterprises of China National Petroleum Corporation (CNPC), such as total contract value, average contract value, transaction frequency, contract completion rate, and payment statistics, thereby increasing decision-makers' confidence and control over inter-enterprise transaction decisions. 2. Improved efficiency of transaction decisions: Through data integration and analysis, this invention can quickly and accurately provide comprehensive financial information, enabling enterprises to make decisions and take actions more quickly, thereby improving transaction efficiency and flexibility. 3. Provided comprehensive financial information support: This invention can integrate financial data from CNPC's financial system database into key indicators, providing enterprises with comprehensive and accurate financial information support, helping enterprises understand the financial status of their partners and identify potential risks and opportunities. 4. Real-time monitoring and early warning: This invention has the function of real-time monitoring of enterprise transaction data, which can promptly detect abnormalities and provide early warnings, helping enterprises to take risk prevention and response measures, thereby improving the security and reliability of transactions. 5. Optimized Transaction Strategies: Through in-depth analysis of transaction data, this invention can provide enterprises with flexible suggestions for adjusting transaction strategies and optimizing business operations, helping them improve transaction efficiency and competitiveness. In summary, the above-mentioned invention patents can improve the accuracy and efficiency of transaction decisions, provide comprehensive financial information support, real-time monitoring and early warning, and optimize transaction strategies, offering significant effects and value to enterprises' transaction decisions. Furthermore, this method can also help enterprises better understand their own and their partners' financial situations, enabling them to better understand their financial condition and identify potential risks and opportunities.

[0105] Figure 3 A schematic diagram of a transaction processing device for a business provided in this application is shown below. Figure 3 As shown, the transaction processing device 30 for the business provided in this embodiment includes:

[0106] The acquisition module 31 is used to acquire data export requests and acquire data to be processed based on the data export requests; wherein, the data to be processed is the business data between the current user and the target object.

[0107] The mining module 32 is used to mine the data to be processed according to a preset data mining algorithm to determine the correlation between the data to be processed.

[0108] The prediction module 33 is used to perform business prediction on the data to be processed based on the correlation relationship and obtain prediction result information; wherein, the prediction result information represents the degree of completion of the business.

[0109] The generation module 34 is used to generate business strategy information for the business based on the prediction results; wherein, the business strategy information represents the strategy that the current user needs to execute at the current time.

[0110] This application provides another business transaction processing apparatus, in Figure 3 Based on the illustrated embodiment, the mining module 32 is specifically used for:

[0111] Import the preset library.

[0112] Based on a pre-defined database and a pre-defined data mining algorithm, the data to be processed is mined to obtain mining results information, which includes the values ​​of multiple pre-defined indicators.

[0113] Based on the mining results, the data to be processed is grouped to obtain multiple groups of data, and the correlation between the data in each group is determined.

[0114] In one possible implementation, prediction module 33 is specifically used for:

[0115] Based on the correlation between the data sets, a pre-set prediction model is used to make business predictions on the data to be processed, and the completion rates of various types of indicators are obtained.

[0116] Based on the completion rate of each type of indicator, predictive results are generated.

[0117] In one possible implementation, module 34 is specifically used for:

[0118] Based on the forecast results, determine the risk level of the business.

[0119] Based on the risk level, generate business strategy information.

[0120] In one possible implementation, the device is also specifically used for:

[0121] The system monitors the data to be processed and generates an early warning signal if it determines that the data exceeds a preset threshold range.

[0122] In one possible implementation, the device is also specifically used for:

[0123] Determine the association strength level of each relationship, and then determine the support and confidence level corresponding to the association strength level.

[0124] Based on the association strength level, the corresponding support and confidence level, generate a heatmap or funnel plot.

[0125] The apparatus provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0126] Figure 4 A schematic diagram of the structure of the electronic device provided in this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0127] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0128] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0129] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0130] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0131] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0132] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0133] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0134] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0135] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0136] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0137] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0138] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0139] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0140] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0141] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A transaction processing method of a service, characterized by, include: Obtain a data export request, and obtain the data to be processed based on the data export request; wherein, the data to be processed is the business data between the current user and the target object; According to the preset data mining algorithm, the data to be processed is mined to determine the correlation relationship of the data to be processed; Based on the aforementioned correlation, business prediction is performed on the data to be processed to obtain prediction result information; wherein, the prediction result information represents the completion degree of the business. Based on the prediction results, business strategy information for the service is generated; wherein, the business strategy information represents the strategy that the current user needs to execute at the current time.

2. The method of claim 1, wherein, The step of mining the data to be processed according to a preset data mining algorithm to determine the correlation relationship of the data to be processed includes: Import the preset library; Based on the preset library and preset data mining algorithm, the data to be processed is mined to obtain mining result information; wherein, the mining result information includes the values ​​of multiple preset indicators; Based on the mining results, the data to be processed is grouped to obtain multiple groups of data, and the correlation between each group of data is determined.

3. The method of claim 2, wherein, The step of performing business prediction on the data to be processed based on the association relationship to obtain prediction result information includes: Based on the correlation between the data sets, a business prediction is performed on the data to be processed using a preset prediction model to obtain the completion rate of each type of preset indicator. Based on the completion rate of each type of indicator, predictive results are generated.

4. The method according to claim 3, characterized in that, The step of generating business strategy information based on the prediction results includes: Based on the prediction results, the risk level of the business is determined; Based on the risk level, business strategy information is generated.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: The system monitors the data to be processed, and if it is determined that the data exceeds a preset threshold range, an early warning signal is generated.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: Determine the association strength level of each association, and determine the support and confidence level corresponding to the association strength level; A heatmap or funnel plot is generated based on the association strength level, the support and confidence level corresponding to the association strength level.

7. A transaction processing apparatus for a business, characterized in that, include: The acquisition module is used to acquire data export requests and acquire data to be processed based on the data export requests; wherein, the data to be processed is the business data between the current user and the target object; The data mining module is used to perform data mining processing on the data to be processed according to a preset data mining algorithm to determine the correlation relationship of the data to be processed. The prediction module is used to perform business prediction on the data to be processed based on the correlation relationship, and obtain prediction result information; wherein, the prediction result information represents the completion degree of the business. The generation module is used to generate business strategy information for the service based on the prediction result information; wherein the business strategy information represents the strategy that the current user needs to execute at the current time.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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