Risk report generation method and device, equipment, medium and program product

Abstract

The invention provides a risk report generation method which can be applied to the technical field of artificial intelligence. The risk report generation method comprises the following steps: in response to a received first instruction of a user, collecting target data from at least one channel; obtaining a historical risk report related to the user; analyzing the historical risk report, and determining reference information for guiding generation of the risk report; processing the target data based on the reference information to generate a first risk report; wherein the first risk report is used for providing preview for the user; in response to a received second instruction of the user, generating a target risk report based on the first risk report; wherein the second instruction is fed back by the user based on the first risk report. The invention further provides a risk report generation device and equipment, a storage medium and a program product.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically to a risk report generation method, apparatus, device, medium and program product. Background Art

[0002] As the scale of enterprises expands and the complexity of their business increases, risk management becomes an indispensable part of enterprise operations. People can generate corresponding risk reports by collecting relevant data to analyze and evaluate risks, thereby achieving the purpose of risk avoidance.

[0003] The generation of traditional risk reports relies on manual data collection, risk analysis and report writing, which is a time-consuming, labor-intensive and error-prone process. In recent years, although artificial intelligence technology has been widely used in the field of report generation, it is often limited by preset templates and algorithms, so that the generated reports are generated according to certain fixed rules, with a low degree of intelligence and difficulty in meeting the personalized needs of users. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a risk report generation method, apparatus, device, medium and program product for improving the efficiency of risk report generation.

[0005] According to a first aspect of the present disclosure, a risk report generation method is provided, comprising: in response to receiving a first instruction from a user, collecting target data from at least one channel; obtaining historical risk reports related to the user; parsing the historical risk reports to determine reference information for guiding risk report generation; processing the target data based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to the user; in response to receiving a second instruction from the user, generating a target risk report based on the first risk report; wherein the second instruction is feedback from the user based on the first risk report.

[0006] According to an embodiment of the present disclosure, the first instruction includes a first form and a second form, the amount of information contained in the first form is greater than the amount of information contained in the second form, and in response to receiving the first instruction from the user, collecting target data from at least one channel includes: parsing the first instruction to obtain data collection information and report information; determining the form of the first instruction based on the data collection information and the report information; when the first instruction is in the first form, collecting target data from at least one channel based on the first instruction; when the first instruction is in the second form, collecting target data based on the user's historical behavior information.

[0007] According to an embodiment of the present disclosure, when the first instruction is in the second form, collecting target data based on the user's historical behavior information includes: determining the user's demand information by analyzing the historical behavior information; and collecting target data based on the demand information.

[0008] According to an embodiment of the present disclosure, the reference information includes first information and second information, and the historical risk report is parsed to determine the reference information used to guide the generation of the risk report, including: analyzing the historical risk report from the content dimension to obtain the first information, wherein the first information is used to characterize the analysis dimension of the target data; analyzing the historical risk report from the format dimension to obtain the second information, wherein the second information is used to characterize the generation format of the risk report.

[0009] According to an embodiment of the present disclosure, target data is processed based on reference information to generate a first risk report, including: grouping and analyzing the target data according to the first information to obtain multiple groups of risk analysis results; determining a target report template according to the second information; and integrating the multiple groups of risk analysis results based on the report template to generate a first risk report.

[0010] According to an embodiment of the present disclosure, determining a target report template based on the second information includes: selecting a candidate report template from a preset template library based on the second information; adjusting and optimizing the candidate report template to obtain a target report template; wherein adjusting and optimizing the candidate report template includes modifying at least one of the content display format, content layout, and visual elements.

[0011] According to an embodiment of the present disclosure, the second instruction includes a modification instruction or a confirmation instruction. In response to receiving the second instruction from the user, a target risk report is generated based on the first risk report, including: parsing the received second instruction; when the second instruction is a modification instruction, modifying the first risk report based on the second instruction; when the second instruction is a confirmation instruction, determining that the first risk report is a target risk report.

[0012] According to an embodiment of the present disclosure, the method further includes: obtaining the user's authorization to obtain historical risk reports and / or historical behavior information; after obtaining the user's authorization, obtaining the historical risk reports and / or historical behavior information.

[0013] A second aspect of the present disclosure provides a risk report generating device, comprising: a collection module, for collecting target data from at least one channel in response to receiving a first instruction from a user; an acquisition module, for acquiring historical risk reports related to the user; a determination module, for parsing historical report information, and determining reference information for guiding risk report generation; a first generation module, for processing the target data based on the reference information, and generating a first risk report; wherein the first risk report is used to provide a preview to the user; and a second generation module, for generating a target risk report based on the first risk report in response to receiving a second instruction from the user; wherein the second instruction is feedback from the user based on the first risk report.

[0014] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0015] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0016] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0018] Figure 1 The application scenario diagram of the risk report generation method, apparatus, device, medium and program product according to the embodiment of the present disclosure is schematically shown;

[0019] Figure 2 A flow chart schematically shows a method for generating a risk report according to an embodiment of the present disclosure;

[0020] Figure 3 A flowchart schematically illustrates, in response to receiving a first instruction from a user, collecting target data from at least one channel according to an embodiment of the present disclosure;

[0021] Figure 4 A flowchart of collecting target data based on historical behavior information of a user when the first instruction is in the second form according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 5 A flowchart schematically illustrates parsing a historical risk report and determining reference information for guiding risk report generation according to an embodiment of the present disclosure;

[0023] Figure 6 A flowchart for processing target data based on reference information and generating a first risk report according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 7 A flowchart of determining a target report template according to second information according to an embodiment of the present disclosure is schematically shown;

[0025] Figure 8A flowchart schematically illustrates, in response to receiving a second instruction from a user, generating a target risk report based on a first risk report according to an embodiment of the present disclosure;

[0026] Fig. 9 A schematic diagram shows a structural block diagram of a risk report generating device according to an embodiment of the present disclosure; and

[0027] Fig.10 A block diagram of an electronic device suitable for implementing a risk report generating method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0029] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0030] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0031] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0032] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0033] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0034] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0035] An embodiment of the present disclosure provides a risk report generation method, comprising: in response to receiving a first instruction from a user, collecting target data from at least one channel; obtaining historical risk reports related to the user; parsing the historical report information to determine reference information for guiding risk report generation; processing the target data based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to the user; in response to receiving a second instruction from the user, generating a target risk report based on the first risk report; wherein the second instruction is feedback from the user based on the first risk report.

[0036] Figure 1 The application scenario diagram of the risk report generation method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0037] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0038] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0039] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0040] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0041] It should be noted that the risk report generation method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the risk report generation device provided in the embodiment of the present disclosure can generally be set in the server 105. The risk report generation method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the risk report generation device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0042] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0043] The following will be based on Figure 1 The scene described by Figure 2~Figure 6 The risk report generating method of the disclosed embodiment is described in detail.

[0044] Figure 2 The flowchart of the risk report generation method according to the embodiment of the present disclosure is schematically shown.

[0045] like Figure 2 As shown, the risk report generation of this embodiment includes operations S210 to S250.

[0046] In operation S210 , in response to receiving a first instruction from a user, target data is collected from at least one channel.

[0047] In some embodiments, a user can issue a first instruction through a front-end interface or an API interface to instruct the system to generate a risk report. The system identifies and parses the received first instruction, and collects target data from at least one preset channel based on the first instruction. Among them, the preset channels include at least internal channels and external channels. Internal channels refer to data sources within the enterprise, such as internal transaction data, business management data, or other information generated and stored within the enterprise. External channels refer to the ways in which enterprises obtain data from the outside. These data usually come from market environment, industry trends, opponents, policies and regulations, etc. The data from external channels may include, for example, market research data, industry report data, and data released by government departments.

[0048] In operation S220, a historical risk report related to the user is obtained.

[0049] In some embodiments, the historical risk reports related to the user include at least two aspects, one is the historical risk reports previously created by the user, and the other is the historical risk reports that the user has frequently browsed. These historical risk reports can effectively reflect the user's areas of concern and business priorities, interests and concerns, and information acquisition habits, and play a guiding role in the subsequent analysis and generation of risk reports, thereby improving the flexibility of risk report generation.

[0050] In operation S230, the historical risk report is parsed to determine reference information for guiding the generation of the risk report.

[0051] In some embodiments, historical risk reports are parsed from different dimensions to obtain the user's focus and format preferences in historical risk reports, providing reference for this risk report. For example, by referring to historical risk reports, it is possible to identify which dimensions are key elements in past risk analysis, thereby improving the pertinence and relevance of risk reports, or, based on the obtained user format preferences, guide the generation format of this report to improve the readability and comprehensibility of the report.

[0052] During the specific implementation process, historical risk reports can also be flexibly acquired and parsed based on the relevant information given by the first instruction. For example, if the report type to be generated in the first instruction is A, then in operations S220 and S230, historical risk reports of report type A can be acquired and parsed, thereby improving the flexibility and accuracy of subsequently generated risk reports.

[0053] In operation S240, the target data is processed based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to the user.

[0054] In some embodiments, the historical risk report and the target data can be transferred to a pre-trained large language model, and the natural language processing capabilities and knowledge base of the large language model are used to perform operations S230 and S240. Based on the large language model, the target data is deeply analyzed, for example, the analysis results can be obtained from the target data by understanding the meaning of the text, identifying patterns, predicting trends, etc., and the first risk report is automatically generated. Among them, the content of the first risk report may include a risk overview, impact assessment, recommended countermeasures, etc. The content of the first risk report is presented in an easy-to-understand manner, for example, the analysis results can be displayed in the form of icons, lists, summaries, etc., and the first risk report can be provided to the user for preview.

[0055] In operation S250, in response to receiving a second instruction from the user, a target risk report is generated based on the first risk report; wherein the second instruction is an instruction output by the user based on the first risk report.

[0056] In some embodiments, before finally generating the target risk report, the user first previews the first risk report to adjust the report content so that the finally generated target risk report better meets the user's needs.

[0057] In the specific implementation process, the second instruction may include, for example, a confirmation instruction or a modification instruction. By identifying and parsing the second instruction, the user's opinion on the first risk report is obtained. In the case where the second instruction is a confirmation instruction, the first risk report is generated as the target risk report. In the case where the second instruction is a modification instruction, the first risk report is modified based on the second instruction until the user issues a confirmation instruction.

[0058] The risk report generation method provided by the embodiment of the present disclosure realizes an automated process from data collection to risk report generation, effectively improves the efficiency and accuracy of risk report generation, and analyzes the target data based on a large language model, which can deeply mine the potential risk information in the data and improve the objectivity of the risk report. The present disclosure obtains historical risk reports related to users and analyzes the historical risk reports to obtain the user's preference for risk reports, thereby generating risk reports according to the user's preferences, meeting the personalized needs of different users, and improving the user experience.

[0059] Figure 3 The flowchart of collecting target data from at least one channel in response to receiving a first instruction from a user according to an embodiment of the present disclosure is schematically shown.

[0060] like Figure 3 As shown, in response to receiving a first instruction from a user, collecting target data from at least one channel in this embodiment includes operations S310 to S340.

[0061] In operation S310, the first instruction is parsed to obtain data collection information and report information.

[0062] In some embodiments, the first instruction includes at least some user requirements, for example, it may include data collection information and report information, wherein the data collection information is used to guide the system to collect data, for example, it may include data collection scope, data type, etc., and the report information is used to guide the system to generate a risk report, such as the content that the risk report needs to reflect and the requirements for the risk report.

[0063] In operation S320 , a form of the first instruction is determined based on the data collection information and the report information.

[0064] In some embodiments, the first instruction includes a first form and a second form, wherein the amount of information contained in the first form is greater than the amount of information contained in the second form. If the data collection information in the first instruction matches the risk report information and the data collection information is relatively clear, the first instruction is in the first form. If the data collection information in the first instruction is vague or the data collection information does not match the risk report information (for example, the data given in the data collection information cannot meet the requirements of the risk report information, etc.), the first instruction is in the second form. Different target data collection methods are used for first instructions of different forms.

[0065] In the specific implementation process, for example, the first instruction can be parsed through natural language processing (NLP) or preset parsing rules to extract data collection information and report information respectively, compare the data collection information with the report information to obtain a matching relationship between the two, and perform ambiguity detection on the data collection information through NLP technology.

[0066] In operation S330 , when the first instruction is in the first form, target data is directly collected from a designated channel based on the first instruction.

[0067] In some embodiments, if the data collection information ambiguity detection passes and the data collection information matches the report information, the first instruction is determined to be in the first form. Based on the data collection information, at least one suitable collection channel (such as an internal database, an API interface, a third-party data source, etc.) is determined, and a suitable data collection tool is used or a data collection script is written to obtain the target data from the specified channel.

[0068] In operation S340 , when the first instruction is in the second form, target data is collected based on the historical behavior information of the user.

[0069] In some embodiments, when the first instruction is in the second form, the data collection information needs to be improved according to the user's historical behavior information to improve the accuracy of the risk report finally generated.

[0070] The risk report generation method provided by the present disclosure analyzes the first instruction and selects the corresponding data collection method, thereby effectively improving the efficiency of data collection. When the data collection information in the first instruction is unclear, the data collection information is automatically improved based on the user's historical behavior data, which helps to reduce the blindness and uncertainty in the data collection process, thereby effectively improving the accuracy of target data collection.

[0071] It should be noted that, in the embodiments of the present disclosure, before obtaining the user's information, the user's consent or authorization may be obtained.

[0072] For example, before executing operation S220, a request to obtain a historical risk report related to the user may be issued to the user, and if the user agrees or authorizes to obtain the historical risk report, operation S220 is executed. Also, before executing operation S340, a request to obtain historical behavior information may be issued to the user, and if the user agrees or authorizes to obtain the historical risk report, operation S340 is executed.

[0073] Figure 4 The flowchart of collecting target data based on the user's historical behavior information when the first instruction is in the second form according to an embodiment of the present disclosure is schematically shown.

[0074] like Figure 4 As shown, in this embodiment, when the first instruction is in the second form, collecting target data based on the user's historical behavior information includes operations S410 to S420.

[0075] In operation S410, user demand information is determined by analyzing historical behavior information.

[0076] In some embodiments, the user's behavior data when viewing, editing, and downloading reports can be collected through system logs, clickstream analysis, and other means, such as the pages most frequently visited by users, the parts where users stay the longest, etc. Through the reverse attack user's behavior information, the user's interest preferences, reporting habits, etc. can be identified to obtain the user's demand information, where the user's demand information can include, for example, the content points that the user pays attention to in different types of reports, such as whether they pay special attention to financial data, market trends, or project progress.

[0077] In operation S420, target data is collected based on the demand information.

[0078] In some embodiments, second data collection information is generated according to the demand information, and the second data collection information includes the target data type and range to be collected, which is a further supplement to the data collection information in the first instruction. According to the data collection information in the first instruction and the second collection information, at least one suitable collection channel (such as an internal database, an API interface, a third-party data source, etc.) is determined, and a suitable data collection tool or a data collection script is written to obtain the target data from the specified channel.

[0079] The risk report generation method provided by the present disclosure analyzes the user's historical behavior information to understand the user's real needs and preferences, and further supplements the data collection information to improve the comprehensiveness and accuracy of the data collection information, thereby improving the accuracy of the target data collection to ensure the accuracy of the risk report finally generated.

[0080] Figure 5 A flowchart for parsing a historical risk report and determining reference information for guiding the generation of a risk report according to an embodiment of the present disclosure is schematically shown.

[0081] like Figure 5 As shown, in this embodiment, parsing the historical risk report and determining the reference information for guiding the generation of the risk report includes operations S510 to S520.

[0082] In operation S510, the historical risk report is analyzed from a content dimension to obtain first information, wherein the first information is used to characterize an analysis dimension of target data.

[0083] In operation S520, the historical risk report is analyzed from a format dimension to obtain second information, wherein the second information is used to characterize a generation format of the risk report.

[0084] In some embodiments, the large language model analyzes the historical risk report according to different dimensions to obtain the first information and the second information.

[0085] On the one hand, the big language model analyzes historical risk reports from the content dimension to obtain the first information, including: collecting all relevant risk descriptions, classifications, impact levels, frequency of occurrence and other key information from historical risk reports, and using natural language processing (NLP) technology, such as entity recognition and keyword extraction, to automatically extract key risk features from the text, such as risk type (credit risk, market risk, operational risk, etc.), impact scope (region, department, project, etc.), severity, etc. Based on the extracted information, a multi-dimensional framework for risk analysis is constructed. These dimensions may include risk type, impact object, time period, geographic location, etc., which can reflect the content points that users are concerned about in different types of reports and are used for subsequent target data grouping analysis.

[0086] On the other hand, the large language model analyzes historical risk reports from the format dimension to obtain second information, including: analyzing the format of historical risk reports, including report structure (introduction, text, conclusion, recommendations, etc.), chart types (bar charts, pie charts, line charts, etc.), data presentation methods (tables, lists, etc.), to obtain the main patterns and trends in users' historical risk reports (i.e., second information), which may reflect users' specific reporting needs and preferences.

[0087] Figure 6 The flowchart of generating a first risk report based on reference information processing target data according to an embodiment of the present disclosure is schematically shown.

[0088] like Figure 6 As shown, in this embodiment, the process of processing target data based on reference information and generating a first risk report includes operations S610 to S630.

[0089] In operation S610, target data is grouped and analyzed according to the first information to obtain multiple groups of risk analysis results.

[0090] In some embodiments, the target data is first preprocessed, and a grouping strategy is formulated according to the analysis dimension determined in the first information. The preprocessed target data is grouped according to the grouping strategy and risk analysis is performed separately to obtain multiple groups of risk analysis results. Each group of risk analysis results can at least reflect the risk situation of one dimension. The new type of preprocessing of the target data may include cleaning, conversion, and standardization of the target data.

[0091] In operation S620, a target report template is determined according to the second information.

[0092] In some embodiments, the second information may include, for example, the page layout, font style, color scheme, icon type, etc. of the risk report. The target report template is designed based on the second information so that the target report template can meet the user's reporting needs and preferences.

[0093] In operation S630, multiple groups of risk analysis results are integrated based on the report template to generate a first risk report.

[0094] In some embodiments, multiple groups of risk analysis results are filled in according to the construction of the target report template, and appropriate icon types are selected for visual display based on the second information to enhance the persuasiveness and readability of the risk report.

[0095] The risk report generation method provided by the present disclosure can effectively shorten the time of risk analysis through automated information extraction and group analysis of target data, and can refine the analysis dimensions on the other hand, making the analysis process more focused, reducing unnecessary interference, and improving the accuracy of the analysis results, thereby effectively improving the efficiency and accuracy of risk report generation. In addition, by determining the target report template through the second information, the flexibility of target report generation can be effectively improved, so that the generated risk report can meet the different needs and preferences of users and improve the user experience.

[0096] Figure 7 A flowchart of determining a target report template according to second information according to an embodiment of the present disclosure is schematically shown.

[0097] like Figure 7 As shown, the method of determining a target report template according to the second information in this embodiment includes operations S710 to S720.

[0098] In operation S710, a candidate report template is selected from a preset template library according to second information.

[0099] In operation S720, the candidate report template is adjusted and optimized to obtain a target report template.

[0100] In some embodiments, the second information may include, for example, common elements and difference elements in the historical report information, wherein the common elements are reusable report structures in the historical report information, and these report structures may include, for example, fixed report structures, styles, and layouts.

[0101] The preset template library contains report templates of various types and formats. These templates are pre-designed based on past experience or industry standards.

[0102] The extracted reusable report structure is compared with each report template in the template library, and the similarity between the report structure and each report template is evaluated. Based on the similarity evaluation result, the report template (i.e., the candidate report template) that best matches the second information is selected. At least part of the content in the selected report template (i.e., the candidate report template) is adjusted and optimized based on the difference elements to make the report template more in line with user needs. The adjusted report template is the target report template. Among them, adjusting and optimizing the candidate report template at least includes modifying at least one of the content display form (such as text, table, icon, etc.), content layout (such as title hierarchy, line spacing, element spacing, etc.), and visual elements (such as color matching, font size, etc.).

[0103] The risk report generation method provided by the present disclosure can effectively shorten the preparation time of the target report template and effectively improve the generation efficiency of the target report template by selecting and adjusting the existing report template. In addition, by adjusting the selected report template based on the second information, the target report template can be personalized, meeting different report requirements and improving the flexibility of risk report generation.

[0104] Figure 8 The flowchart of generating a target risk report based on a first risk report in response to receiving a second instruction from a user according to an embodiment of the present disclosure is schematically shown.

[0105] like Figure 8 As shown, in response to receiving a second instruction from a user, generating a target risk report based on the first risk report in this embodiment includes operations S810 to S830.

[0106] In operation S810 , the received second instruction is parsed.

[0107] In some embodiments, the second instruction is a user's feedback based on the first risk report. For example, the first risk report is displayed in the first area of ​​the front end, and after viewing the first risk report, the user issues a second instruction in the second area, wherein the second instruction includes a modification instruction and a confirmation instruction. When the user believes that the first risk report needs to be adjusted, the modification instruction is sent; when the user believes that the first risk report does not need to be adjusted, the confirmation instruction is sent. By parsing the received second instruction, the second instruction type (i.e., modification instruction or confirmation instruction) and the specific modification requirements or confirmation information are identified.

[0108] In operation S820, if the second instruction is a modification instruction, the first risk report is modified based on the second instruction.

[0109] In some embodiments, when the second instruction is a modification instruction, a modification process is entered, specific modification requirements are extracted from the modification instruction, and the first risk report is modified based on the modification requirements. The modification requirements may include, for example, modifying the content of the first risk report and / or modifying the format of the first risk report. When the content of the first risk report needs to be modified, it may be necessary to re-acquire the target data based on the modification requirements and analyze the newly acquired target data to obtain the risk analysis results.

[0110] After the modification is completed, the modified first risk report is resent to the first area of ​​the front end for user preview until the received second instruction is a confirmation instruction, otherwise the above operations S810 to S820 are repeated.

[0111] In operation S830, when the second instruction is a confirmation instruction, the first risk report is determined to be a target risk report.

[0112] In some embodiments, if the second instruction is a confirmation instruction, a confirmation process is entered, and the first risk report is output as the final output target risk report.

[0113] The risk report generation method provided by the present disclosure provides a preview to the user, so that the user can flexibly modify and confirm the risk report according to actual needs, thereby improving the accuracy and reliability of the finally generated report.

[0114] Based on the above risk report generation method, the present disclosure also provides a risk report generation device. Fig. 9 The device is described in detail.

[0115] Fig. 9 The structure block diagram of the risk report generating device according to the embodiment of the present disclosure is schematically shown.

[0116] like Fig. 9 As shown, the risk report generating device 900 of this embodiment includes a collection module 910 , an acquisition module 920 , a determination module 930 , a first generation module 940 and a second generation module 950 .

[0117] The collection module 910 is used to collect target data from at least one channel in response to receiving a first instruction from a user. In one embodiment, the collection module 910 can be used to perform the operation S210 described above, which will not be described in detail here.

[0118] The acquisition module 920 is used to acquire a historical risk report related to the user. In one embodiment, the acquisition module 920 can be used to perform the operation S220 described above, which will not be described in detail here.

[0119] The determination module 930 is used to parse the historical risk report and determine the reference information for guiding the generation of the risk report. In one embodiment, the determination module 930 can be used to perform the operation S230 described above, which will not be described in detail here.

[0120] The first generating module 940 is used to process the target data based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to the user. In one embodiment, the first generating module 940 can be used to perform the operation S240 described above, which will not be repeated here.

[0121] The second generating module 950 is used to generate a target risk report based on the first risk report in response to receiving a second instruction from the user; wherein the second instruction is a feedback from the user based on the first risk report. In one embodiment, the second generating module 950 can be used to perform the operation S250 described above, which will not be described in detail here.

[0122] According to an embodiment of the present disclosure, any multiple modules of the acquisition module 910, the acquisition module 920, the determination module 930, the first generation module 940, and the second generation module 950 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the acquisition module 910, the acquisition module 920, the determination module 930, the first generation module 940, and the second generation module 950 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the collection module 910 , the acquisition module 920 , the determination module 930 , the first generation module 940 , and the second generation module 950 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0123] Fig.10 A block diagram of an electronic device suitable for implementing a risk report generating method according to an embodiment of the present disclosure is schematically shown.

[0124] like Fig.10As shown, the electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage part 1008 to a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include an onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0125] In RAM 1003, various programs and data required for the operation of electronic device 1000 are stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 1002 and / or RAM 1003. It should be noted that the program can also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0126] According to an embodiment of the present disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to the bus 1004. The electronic device 1000 may further include one or more of the following components connected to the input / output (I / O) interface 1005: an input portion 1006 including a keyboard, a mouse, etc.; an output portion 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 1008 including a hard disk, etc.; and a communication portion 1009 including a network interface card such as a LAN card, a modem, etc. The communication portion 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage portion 1008 as needed.

[0127] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0128] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above and / or one or more memories other than ROM 1002 and RAM 1003.

[0129] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the risk report generation method provided by the embodiment of the present disclosure.

[0130] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 1001. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0131] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 1009, and / or installed from the removable medium 1011. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0132] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 1009, and / or installed from the removable medium 1011. When the computer program is executed by the processor 1001, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0133] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0134] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0135] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0136] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for generating a risk report, characterized in that: The method comprises: In response to receiving a first instruction from a user, collecting target data from at least one channel; Obtaining historical risk reports related to the user; Parsing the historical risk report to determine reference information for guiding the generation of the risk report; Processing the target data based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to a user; In response to receiving a second instruction from the user, a target risk report is generated based on the first risk report; wherein the second instruction is feedback from the user based on the first risk report.

2. The risk report generation method according to claim 1, characterized in that: The first instruction includes a first form and a second form, the amount of information included in the first form is greater than the amount of information included in the second form, and in response to receiving the first instruction from the user, collecting target data from at least one channel includes: Parsing the first instruction to obtain data collection information and report information; determining a form of a first instruction based on the data collection information and the report information; When the first instruction is in a first form, collecting target data from at least one channel based on the first instruction; When the first instruction is in the second form, target data is collected based on the historical behavior information of the user.

3. The risk report generation method according to claim 2, characterized in that: When the first instruction is in the second form, collecting target data based on the historical behavior information of the user includes: Determining the user's demand information by analyzing the historical behavior information; Target data is collected based on the demand information.

4. The risk report generation method according to claim 1, characterized in that: The reference information includes first information and second information, and the parsing of the historical risk report to determine the reference information for guiding the generation of the risk report includes: Analyze the historical risk report from a content dimension to obtain first information, wherein the first information is used to characterize an analysis dimension of the target data; The historical risk report is analyzed from a format dimension to obtain second information, wherein the second information is used to characterize the generation format of the risk report.

5. The risk report generation method according to claim 1 or 4, characterized in that: The step of processing the target data based on the reference information to generate a first risk report includes: Perform group analysis on the target data according to the first information to obtain multiple groups of risk analysis results; Determine a target report template according to the second information; Multiple groups of risk analysis results are integrated based on the report template to generate a first risk report.

6. The risk report generation method according to claim 5, characterized in that: The determining a target report template according to the second information includes: selecting a candidate report template from a preset template library according to the second information; Adjusting and optimizing the candidate report template to obtain a target report template; The adjusting and optimizing of the candidate report template includes modifying at least one of the content display format, content layout, and visual elements.

7. The risk report generation method according to claim 1, characterized in that: The second instruction includes a modification instruction or a confirmation instruction, and in response to receiving the second instruction from the user, generating a target risk report based on the first risk report includes: Parsing the received second instruction; In the case where the second instruction is a modification instruction, modifying the first risk report based on the second instruction; When the second instruction is a confirmation instruction, the first risk report is determined to be a target risk report.

8. The method according to claim 1, characterized in that The method further comprises: Obtaining the user's authorization to obtain the historical risk report and / or historical behavior information; After obtaining user authorization, the historical risk report and / or the historical behavior information is obtained.

9. A risk report generating device, characterized in that: The device comprises: a collection module, configured to collect target data from at least one channel in response to receiving a first instruction from a user; An acquisition module, used to acquire a historical risk report related to the user; A determination module, used to parse the historical risk report and determine reference information for guiding the generation of the risk report; A first generating module is configured to process the target data based on the reference information to generate a first risk report; wherein the first risk report is used to provide a preview to a user; and The second generating module is used to generate a target risk report based on the first risk report in response to receiving a second instruction from the user; wherein the second instruction is feedback from the user based on the first risk report.

10. An electronic device comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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