Demand compiling method and device, electronic equipment and storage medium

Abstract

The invention provides a demand compiling method which can be applied to the technical field of artificial intelligence, and particularly relates to the fields of business architecture, big data technology, machine learning technology and the like. The method comprises the steps of determining target business model information related to demand design information from existing business model information according to the demand design information in response to the received demand design information related to a business demand to be compiled; inputting the target business model information into a risk point pushing model, and generating target risk check point information related to the target business model information; and integrating the target business model information and the target risk check point information to obtain a business demand book which is automatically compiled for the demand design information. The invention further provides a demand compiling device, electronic equipment and a storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to business architecture, big data technology, machine learning technology and other fields, and more specifically to a demand writing method, device, electronic device and storage medium. Background Art

[0002] With the development of artificial intelligence technology, automated requirements writing methods have a foundation for implementation. However, automated requirements writing methods still face some challenges, such as difficulty in ensuring the accuracy and completeness of generated requirements documents. Summary of the Invention

[0003] In view of the above problems, the present disclosure provides a method, device, electronic device and storage medium for writing requirements.

[0004] According to the first aspect of the present disclosure, a requirement writing method is provided, including: in response to receiving requirement design information related to a business requirement to be written, determining target business model information related to the requirement design information from existing business model information based on the requirement design information; inputting the target business model information into a risk point push model to generate target risk checkpoint information related to the target business model information; and integrating the target business model information and the target risk checkpoint information to obtain a business requirement book automatically written for the requirement design information.

[0005] According to an embodiment of the present disclosure, the demand design information includes the demand item name and the transformation content, and the target business model information includes the target business architecture information; according to the demand design information, determining the target business model information related to the demand design information from the existing business model information includes: according to the demand item name, analyzing the existing business architecture model, and determining a candidate business architecture model in which the existing domain layer information and the existing value stream layer information in the existing business architecture model match the demand item name, and the candidate business architecture model also includes the existing activity layer information and the existing task layer information; and performing similarity matching between the existing activity layer information and the existing task layer information and the transformation content respectively, determining a first target business architecture model whose similarity is higher than the corresponding first threshold, and obtaining the target business architecture information.

[0006] According to an embodiment of the present disclosure, determining target business model information related to the demand design information from existing business model information based on the demand design information also includes: obtaining historical architecture analysis results obtained by analyzing the existing business architecture model, wherein the historical architecture analysis results include historical transformation information; performing similarity matching on the historical transformation information and the transformation content to obtain target historical transformation information with a similarity higher than a second threshold; and determining a second target business architecture model based on the existing business architecture model used to analyze and obtain the target historical transformation information to obtain the target business architecture information.

[0007] According to an embodiment of the present disclosure, the target business model information includes target business product information and target business market information; according to the demand design information, determining the target business model information related to the demand design information from the existing business model information also includes: according to the target activity layer information and the target task layer information in the target business architecture information, determining the basic product information associated with the target activity layer information and the target task layer information from the existing product model to obtain the target business product information; and, if authorized, determining the third-party information and channel information associated with the target activity layer information and the target task layer information from the existing market model to obtain the target business market information.

[0008] According to an embodiment of the present disclosure, the target business model information also includes target demand background information; based on the demand design information, the target business model information related to the demand design information is determined from the existing business model information, including: retrieving a target demand book from the historical demand book that matches the target business product information and target business market information, as well as the target activity layer information and target task layer information in the target business architecture information; and determining the target demand background information based on the background information in the target demand book.

[0009] According to an embodiment of the present disclosure, the target business model information also includes target business process information. According to the demand design information, the target business model information related to the demand design information is determined from the existing business model information, including: adjusting the target activity process information in the target business architecture information according to the transformation content to obtain the target business process information.

[0010] According to an embodiment of the present disclosure, the business requirements document includes a business model part generated based on the target business model information and a risk checkpoint information used to determine the target risk checkpoint information. The risk checkpoint information includes existing business link information, existing applicable business product information, existing business dimension information and existing risk checkpoint information. The existing risk checkpoint information is determined based on at least one of the existing business link information, existing applicable business product information and existing business dimension information.

[0011] According to an embodiment of the present disclosure, the risk point push model is trained by the following method: determining candidate risk checkpoint information from the risk check table based on target business link information, target applicable business product information and target business dimension information related to the target business model information; matching the checkpoint keyword information of the candidate risk checkpoint information with the model keyword information of the target business model information, and determining the candidate risk checkpoint information with mutually matching keywords as the target risk checkpoint information; and in response to determining that the target risk checkpoint information has been supplemented into the risk check library, optimizing the risk point push model using the supplemented risk check library.

[0012] The second aspect of the present disclosure provides a demand writing device, including: a target business model information determination module, which is used to respond to the receipt of demand design information related to the business demand to be written, and determine the target business model information related to the demand design information from the existing business model information according to the demand design information; a target risk checkpoint information generation module, which is used to input the target business model information into the risk point push model to generate target risk checkpoint information related to the target business model information; and a business demand book acquisition module, which is used to integrate the target business model information and the target risk checkpoint information to obtain a business demand book automatically written for the demand design information.

[0013] The third aspect of the present disclosure provides an electronic device, comprising: one or more processors; 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 requirement writing method of the present disclosure.

[0014] 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 requirement writing method of the present disclosure when the computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implements the steps of the requirement writing method of the present disclosure when the computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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:

[0017] Figure 1 A diagram schematically illustrates an application scenario of the method for writing requirements according to an embodiment of the present disclosure;

[0018] Figure 2 A flowchart of a method for writing requirements according to an embodiment of the present disclosure is schematically shown;

[0019] Figure 3 The following schematically illustrates a risk point push model optimized by reinforcement learning based on human feedback according to an embodiment of the present disclosure;

[0020] Figure 4 The overall schematic diagram of the method for writing requirements according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 5 A schematic diagram of a structural block diagram of a demand writing device according to an embodiment of the present disclosure is shown; and

[0022] Figure 6 A block diagram of an electronic device suitable for implementing the requirement writing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, 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 well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0024] 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 presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) 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.

[0026] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning 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.).

[0027] In the technical solutions disclosed herein, 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.

[0028] In scenarios where personal information is used for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure all provide users with corresponding operation portals for them 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 an individual'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.

[0029] An embodiment of the present disclosure provides a method for writing requirements, including: in response to receiving requirement design information related to a business requirement to be written, determining target business model information related to the requirement design information from existing business model information based on the requirement design information; inputting the target business model information into a risk point push model to generate target risk checkpoint information related to the target business model information; and integrating the target business model information and the target risk checkpoint information to obtain a business requirement document automatically written for the requirement design information.

[0030] It should be noted that the requirements writing method and device disclosed herein can also be used in the field of financial technology, and can also be used in any field other than the field of financial technology. The application field of the requirements writing method and device disclosed herein is not limited.

[0031] Figure 1 The application scenario diagram of the requirement writing method according to an embodiment of the present disclosure is schematically shown.

[0032] 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 as a medium for providing 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 or wireless communication links or optical fiber cables.

[0033] A user may use a first terminal device 101, a second terminal device 102, or a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications may be installed on the first terminal device 101, the second terminal device 102, or the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only).

[0034] 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.

[0035] The server 105 may be a server that provides various services, such as a background management server (for example only) that supports 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 received data such as user requests, and feed back processing results (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices.

[0036] It should be noted that the demand writing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the demand writing device provided in the embodiment of the present disclosure can generally be set in the server 105. The demand writing 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 demand writing 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.

[0037] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0038] The following will be based on Figure 1 The scene described by Figures 2 to 4 A method for writing requirements in the disclosed embodiment is described in detail.

[0039] Figure 2 The flowchart of the method for writing requirements according to an embodiment of the present disclosure is schematically shown.

[0040] like Figure 2 As shown, the demand writing method of this embodiment includes operations S210 to S230, and the transaction processing method can be executed by the first terminal device 101, the second terminal device 102, the third terminal device 103 or the server 105.

[0041] In operation S210 , in response to receiving requirement design information related to a business requirement to be written, target business model information related to the requirement design information is determined from existing business model information based on the requirement design information.

[0042] According to embodiments of the present disclosure, requirement design information may represent a brief description of the business requirement to be written. For example, requirement design information may include at least one of the following: requirement item name, modification content, use case name, and use case summary, but is not limited thereto. Business model information may represent basic information related to at least one level of architecture, system, product, and market, such as architecture, system, and market, related to building a complete requirement.

[0043] According to an embodiment of the present disclosure, data mining technology can be used to automatically discover the relationship and dependency between demand design information and business model information, and machine learning technology can be used to automatically generate the structure and content of the demand document. Through the above operation S210, a preliminary business demand document can be generated by integrating the target business model information.

[0044] In operation S220 , the target business model information is input into a risk point push model to generate target risk checkpoint information related to the target business model information.

[0045] According to embodiments of the present disclosure, the risk point push model can analyze the target business model information or the aforementioned preliminary business requirements document and output target risk checkpoint information. The risk checkpoint information can be represented as a paragraph or a single word. For example, it can be represented as "When a user applies for AAA, the system needs to check the user's current BBB," etc., but is not limited to this.

[0046] In operation S230 , the target business model information and the target risk checkpoint information are integrated to obtain a business requirement document automatically written based on the demand design information.

[0047] According to an embodiment of the present disclosure, integration may be performed by first integrating target business model information to generate a preliminary business requirement document; then, further integrating the preliminary business requirement document and target risk checkpoints to obtain a final business requirement document.

[0048] For example, the demand writer can preliminarily fill in demand design information such as the demand item name and transformation content, and then automatically expand and write the demand document to obtain the above-mentioned business demand document.

[0049] Through the above-mentioned embodiments of the present disclosure, a business requirement document including risk checkpoints can be automatically generated, thereby enhancing the integrity of the generated business requirement document.

[0050] According to an embodiment of the present disclosure, the above-mentioned demand design information may include the name of the demand item and the transformation content. The above-mentioned target business model information may include target business architecture information. The above-mentioned operation S210 may include: analyzing the existing business architecture model according to the name of the demand item, and determining a candidate business architecture model in which the existing domain layer information and the existing value stream layer information in the existing business architecture model match the name of the demand item, and the candidate business architecture model also includes existing activity layer information and existing task layer information. The existing activity layer information and the existing task layer information are respectively matched with the transformation content for similarity, and the first target business architecture model with a similarity higher than the corresponding first threshold is determined to obtain the target business architecture information.

[0051] According to embodiments of the present disclosure, a business architecture model may include, from top to bottom, a domain layer, a value stream layer, an activity field, and a task layer. Different business architecture models may have one or more domain layers, which may be the same or different. Each domain layer may include one or more value stream layers. Each value stream layer may include one or more activity layers. Each activity layer may include one or more task layers. When matching the target business architecture model with the required design information, the information of each existing business architecture model, from top to bottom, is sequentially matched with the required design information.

[0052] For example, you can first map requirement names to business domains and value streams. Then, within the domain, you can filter for corresponding activity names. The filtering rules might include matching the PDS (Purpose Definition Scope) of the activities and tasks with the transformation content. The first target business architecture model is determined based on whether the similarity exceeds a first threshold.

[0053] According to an embodiment of the present disclosure, the corresponding first threshold can represent the first threshold set for the activity layer and the task layer respectively. The first threshold set for the activity layer and the task layer respectively can be customized according to business needs. The two can be the same or different, and are not limited here.

[0054] For example, when it is determined that the similarity between the transformation content and the existing activity layer information is higher than the first threshold set for the activity layer, and when it is determined that the similarity between the transformation content and the existing task layer information is higher than the first threshold set for the task layer, the corresponding existing business architecture model is determined as the first target business architecture model.

[0055] According to an embodiment of the present disclosure, operation S210 may further include: obtaining historical architecture analysis results obtained by analyzing an existing business architecture model, wherein the historical architecture analysis results include historical transformation information; performing similarity matching on the historical transformation information and the transformation content to obtain target historical transformation information having a similarity greater than a second threshold; determining a second target business architecture model based on the existing business architecture model used to analyze and obtain the target historical transformation information, and obtaining the target business architecture information.

[0056] For example, the historical architecture analysis results previously analyzed by the user can be combined to perform similarity matching between the historical transformation information in previous requirements and the transformation content of the requirements to be written, thereby determining the second target business architecture model based on whether the similarity is higher than a second threshold.

[0057] It should be noted that the second threshold can be customized according to business needs, and its value can be determined based on the first threshold or can be different from the first threshold, which is not limited here.

[0058] Through the above-mentioned embodiments of the present disclosure, automated business architecture analysis can be achieved through the name of the requirement item and the transformation content, thereby reducing the demand for professional requirement analysts and the overall cost of the software development project.

[0059] According to an embodiment of the present disclosure, the target business model information may include target business product information and target business market information. Operation S210 may further include: determining, based on the target activity layer information and target task layer information in the target business architecture information, basic product information associated with the target activity layer information and target task layer information from an existing product model to obtain the target business product information. If authorized, determining, from an existing market model, third-party information and channel information associated with the target activity layer information and target task layer information to obtain the target business market information.

[0060] According to embodiments of the present disclosure, the architecture of a product model can be represented by, but is not limited to, activity-task-task-related basic products. The architecture of a market model can be represented by, but is not limited to, activity-task-task-related customers, activity-task-task-related partners, activity-task-task-related channels, and product-task-customer relationships.

[0061] For example, after analyzing and identifying a matching business architecture, we can combine information such as activities and tasks within it to obtain CPCP (product, customer, partner, channel) information related to the requirements being written, using the existing architecture's activity-task-task relationships, including the underlying products, customers, partners, and channels. We can also obtain information about the systems involved in the requirements being written, by tracing the product-product line-business system line-business system-main application. This allows us to analyze business data such as the products and systems for which the business requirements are being written, based on the requirements design information. By using the product-task-customer relationships within the existing architecture, we can map products to corresponding target customers. This allows us to analyze market data, such as the market prospects for the business requirements being written, based on the requirements design information.

[0062] According to an embodiment of the present disclosure, the market analysis may be supplemented by retrieving similar customer groups in historical demands that are highly similar to the demand to be written.

[0063] It should be noted that the relevant information of the above-mentioned customers, partners, etc. is obtained on the premise of obtaining authorization from the corresponding customers, partners, etc.

[0064] Through the above-mentioned embodiments of the present disclosure, automated product and market analysis can be achieved, providing richer and more complete demand data.

[0065] According to an embodiment of the present disclosure, when the target business model information obtained already includes the target business architecture information, target business product information and target business market information, the transformation points, activities, tasks, products, customers and other information in the demand book of the same period can be retrieved. If multiple elements are similar, demand integration can be considered. Demand integration may include service aggregation, contact integration, and operation integration. Service aggregation is used to explain the changes in basic products, saleable products and product functional characteristics from a product perspective. Service aggregation can also be used to explain the design principles and planning points of business processes from the perspective of business processing procedures. Contact integration is used to explain the design principles and planning points of contacts from the perspective of external channels and internal user operating systems. Operation integration is used to explain the business operation data planning, operation data indicator planning, reporting rules, point-of-sale planning and other business operation-related content after the relevant business is put into production from the perspective of business operations.

[0066] For example, if the activities, tasks, and target customers are similar but the channels are different, you can consider touchpoint integration. If the activities, tasks, and channels are similar but the target customers are corporate customers and individual customers respectively, you can consider service aggregation.

[0067] According to an embodiment of the present disclosure, the target business model information may further include target demand background information. Operation S210 may further include: retrieving from historical demand documents a target demand document that matches the target business product information, target business market information, and target activity layer information and target task layer information in the target business architecture information. Determining the target demand background information based on the background information in the target demand document.

[0068] For example, you can search for similar historical requirements by activities, tasks, CPCP (products, customers, partners, channels) to improve the demand background for the requirements to be written.

[0069] Through the above-mentioned embodiments of the present disclosure, the demand background can be automatically improved, and the integrity of the demand data can be further enhanced.

[0070] According to an embodiment of the present disclosure, the target business model information may further include target business process information. The above operation S210 may further include: adjusting the target activity process information in the target business architecture information according to the transformation content to obtain the target business process information.

[0071] According to an embodiment of the present disclosure, the activity process information may include an activity flow chart. The target business process information may represent a business process chart generated based on the activity flow chart for the requirement to be written.

[0072] For example, through the activities / tasks in the architecture analysis, the tasks, task sequence, and task PDS (purpose, definition, scope) contained in the activity flow chart can be combined with the transformation content to generate the corresponding target business process information.

[0073] Through the above-mentioned embodiments of the present disclosure, the overall business process information can be automatically generated, further improving the integrity of the demand data.

[0074] According to an embodiment of the present disclosure, the business requirements document may include a business model part generated based on the target business model information and a risk checkpoint information used to determine the target risk checkpoint information. The risk checkpoint information includes existing business link information, existing applicable business product information, existing business dimension information and existing risk checkpoint information. The existing risk checkpoint information is determined based on at least one of the existing business link information, existing applicable business product information and existing business dimension information.

[0075] According to embodiments of the present disclosure, the business model can be represented as the aforementioned preliminary business requirements document. The risk checklist can be represented as a three-dimensional table, where the three dimensions are information about existing business segments, information about existing applicable business products, and information about existing business dimensions. Existing risk checkpoint information represents the information in the three-dimensional table.

[0076] According to an embodiment of the present disclosure, a risk checklist can be added to a preliminary business requirements document while it is being generated. Target risk checkpoint information can be represented by adding a check mark to the risk checkpoint information in the risk checklist that is relevant to the requirements to be written, indicating that the risk checklist has been completed.

[0077] According to an embodiment of the present disclosure, the risk point push model can be trained by the following method: candidate risk checkpoint information is determined from a risk check table based on target business link information, target applicable business product information, and target business dimension information related to the target business model information. The checkpoint keyword information of the candidate risk checkpoint information is matched with the model keyword information of the target business model information, and the candidate risk checkpoint information with matching keywords is determined as the target risk checkpoint information. In response to determining that the target risk checkpoint information has been added to the risk check library, the risk point push model is optimized and trained using the added risk check library.

[0078] According to the embodiments of the present disclosure, during the acquisition of target risk business link information, a distillation model can be trained to predict target risk business link information. This process can include word segmentation, model training, model distillation, and model prediction.

[0079] During the word segmentation phase, the generated preliminary business requirements document can be segmented by project name, user scenario, key requirements, opportunity points, and requirement item name to extract key information. Common terms such as "product" and "business" that appear within each requirement category are removed. The unique terms for each requirement category are then derived. For example, unique terms related to customer access include: risk assessment, tolerance, suitability, Category II, exit category, whitelist, blacklist, and graylist.

[0080] During the model training phase, a large number of historical requirement documents that have completed risk checklists can be trained, and key information such as project names, user scenarios, key requirements, opportunity points, and requirement item names in the historical requirement documents can be trained and matched with risk points in the risk checklist.

[0081] During the model distillation phase, knowledge from a trained large model (the teacher model) is transferred to a smaller model (the student model) to make the model easier to interpret and understand. This improves the performance of the smaller model while reducing its size and computational complexity. This is crucial for applications requiring model auditing or explanation. The process involves first training a large teacher model using extensive data and computational resources. Then, based on the teacher model's knowledge, a smaller and more computationally efficient student model is generated using techniques such as pruning, quantization, and low-rank factorization. The teacher model's knowledge is then transferred to the student model through knowledge distillation and model compression. Knowledge distillation guides training by using the teacher model's outputs as the student model's targets. Model compression converts the teacher model's weights into the format required by the student model. Using the distilled knowledge, the student model is trained using a standard backpropagation algorithm to minimize the loss between the student and teacher models. The student model's performance is then evaluated on the test set. This provides a measure of the effectiveness of model distillation and whether the student model has been able to reduce its size and computational complexity while maintaining performance. Finally, the trained student model is deployed in practical applications, such as image recognition, natural language processing, recommendation systems and other fields. In this embodiment, it can be applied to the field of business link prediction.

[0082] In the model prediction stage, the business requirements document that contains the risk checklist that has been written but not filled out is predicted through the characteristic vocabulary of each type of requirements. By predicting the specific business links in the risk checklist, the above-mentioned target risk business link information can be obtained.

[0083] According to the embodiments of the present disclosure, applicable business products are often general terms. A correspondence can be established between the basic products in the business requirements document and applicable business products, such as personal accounts: Class I, Class II, Class III, and dedicated accounts. Based on this correspondence, when obtaining target applicable business product information, the key information in the preliminary business requirements document can be categorized according to the existing applicable business product information in the risk checklist.

[0084] The fundamentally disclosed embodiment may also pre-establish a correspondence between risk dimensions and key information in the preliminary business requirement document, so as to classify the key information in the preliminary business requirement document according to the existing risk dimension information in the risk checklist.

[0085] According to embodiments of the present disclosure, after determining target business process information, target applicable business product information, and target business dimension information based on the preliminary business requirements document, risk checkpoints can be matched. This involves narrowing down the risk checkpoints by targeting the target risk dimension (credit, settlement, funding, channel, public), the target business process (customer access, customer purchase or transaction, customer application, transaction continuation, etc.), and the target applicable business product, to obtain candidate risk checkpoint information. Next, a word segmentation model is used to segment the risk checklist and the preliminary business requirements document, removing stop words and common words. Keywords from different risk dimensions in the risk checklist are then extracted and matched against keywords in the preliminary business requirements document (including the requirement item name, requirement details, and the underlying business process) to ultimately determine the target risk checkpoint information.

[0086] It should be noted that in the process of finally determining the target risk checkpoint information, it is also possible to combine expert experience to conduct a secondary screening of the keywords screened out by the risk checklist, establish a risk keyword library for different risk dimensions, and construct real sample data for training the risk point push model.

[0087] For example, a risk checkpoint might be: When a customer applies for AAA, the system must check their current BBB. Extract the keywords AAA and BBB. If AAA appears in the preliminary business requirements document but there's no content related to BBB checks, match this risk checkpoint and the corresponding risk control measures to the requirement and add them directly to the risk checklist in the business requirements document.

[0088] According to an embodiment of the present disclosure, after the risk checkpoints and corresponding risk control measures are matched to the business requirements document, the requirements writer can also be prompted. The requirements writer can directly confirm or click to modify to improve the risk checkpoint information in the risk checklist. If the requirements writer modifies the risk checkpoints and corresponding risk control measures, the modified risk checkpoints and risk control measures can be added to the risk check library used to provide background data for the risk checklist, and the risk check library can be continuously improved. Afterwards, the risk point push model can be optimized through a business requirements document that includes a risk checklist with information in the supplemented and improved risk check library.

[0089] During the implementation of the present disclosure, the inventors discovered that while models can match risk checkpoints, some or many of these may not align with the goals and intentions of business personnel. This is because business personnel often rely on years of experience when planning intelligently, and they consider many practical factors that cannot be fully quantified through indicators.

[0090] Therefore, for special or abnormal inspections, reinforcement learning reward signals can be introduced to adjust the behavior of the model so that the content generated by the model is more in line with the preferences and actual work needs of business personnel.

[0091] According to an embodiment of the present disclosure, the risk point push model can be optimized through reinforcement learning based on human feedback. The method can include three stages.

[0092] In the first stage, based on the aforementioned business requirements document containing the completed risk checklist, the initial risk point push model is trained.

[0093] The second phase involves creating a reward model for the reinforcement learning system. To construct each training example, the initial risk point push model is fed a business requirements document containing a completed risk checklist and is instructed to generate several risk checkpoints. Business personnel are then asked to rank these generated risk checkpoints from best to worst based on their alignment with the actual situation and their preferences. The reward model is then trained to predict these risk checkpoints. By matching the training risk checkpoints with the risk checkpoints and ranking scores output by the model, the reward model can create a mathematical representation of business personnel's preferences.

[0094] In the final stage, a reinforcement learning loop is created. Proximal Policy Optimization (PPO) is a reinforcement learning algorithm that adjusts the model's behavior by introducing a reward signal, making the content it generates more consistent with human preferences. Specifically, PPO adjusts the model's policy by maximizing expected reward, making it more inclined to choose actions that yield higher rewards. The PPO algorithm can then be used to adjust this policy so that the model is more sensitive to the preferences of business personnel when generating content. In each training set, the risk checkpoint matching model ingests the content of the business requirements document and generates risk checkpoints. Its output is then passed to the reward model, which provides a score to assess its consistency with the preferences of the requirements writer. The risk checkpoint matching model is then updated to create outputs that score higher in the reward model.

[0095] Figure 3 A schematic diagram of a risk point push model optimized by reinforcement learning based on human feedback according to an embodiment of the present disclosure is schematically shown.

[0096] like Figure 3As shown, the risk point push model 300 may include an initial risk point push model 301 and a reward model 303. The initial risk point push model 301 is pre-trained to generate predicted risk checkpoint information 302. This information is then passed to the reward model 303, generating a ranking result 304 based on the preferences of business personnel. The ranking result 304 can then be used to update the initial risk point push model 301 until an output with a higher score in the reward model 303 can be created based on the output of the initial risk point push model 301. When the score of the reward model 303 reaches a certain range, the initial risk point push model 301 can be determined as the optimized risk point push model.

[0097] Through the above-mentioned embodiments of the present disclosure, a set of feasible methods can be provided for digital transformation risk inspection in an efficient and reliable manner, which is conducive to early estimation of prevention and control risks, reasonable planning of resource allocation, and enhanced quality assurance of the implementation of digital transformation solutions.

[0098] Figure 4 The overall schematic diagram of the requirement writing method according to an embodiment of the present disclosure is schematically shown.

[0099] like Figure 4 As shown, based on existing business model information 401, analysis of requirement design information 410, such as the requirement item name and modification content, entered by the requirements writer, yields target business architecture information 411, target business product information 412, target business market information 413, target requirement background information 414, and target business process information 415. This information can then be integrated to produce a preliminary business requirements document 420. By inputting the unfilled risk checklist 402 (with unfilled checkpoint information) and preliminary business requirements document 420 into the risk point push model 430, a filled-in risk checklist 431, with the target risk checkpoint information filled in, is obtained. By integrating preliminary business requirements document 420 and filled-in risk checklist 431, a business requirements document 440, automatically expanded and generated based on the requirement design information 410, is obtained.

[0100] Through the above-mentioned embodiments of the present disclosure, the automated method for generating requirements documents can effectively reduce the time and effort required for manual writing of requirements documents, thereby improving writing efficiency. In addition, the automated requirements writing method can reduce the problems such as negligence and misunderstanding that may occur during the manual writing process, thereby improving the accuracy and consistency of the requirements documents, improving the writing quality, and making the requirements documents easier to update and maintain to adapt to the ever-changing user needs and project environment, thereby facilitating maintenance.

[0101] Based on the above-mentioned requirement writing method, the present disclosure also provides a requirement writing device. Figure 5 The device is described in detail.

[0102] Figure 5 The structural block diagram of the requirement writing device according to an embodiment of the present disclosure is schematically shown.

[0103] like Figure 5 As shown, the requirement writing device 500 of this embodiment includes a target business model information determination module 510 , a target risk checkpoint information generation module 520 and a business requirement document acquisition module 530 .

[0104] Target business model information determination module 510 is configured to, in response to receiving requirement design information related to a business requirement to be written, determine target business model information related to the requirement design information from existing business model information based on the requirement design information. In one embodiment, target business model information determination module 510 may be configured to perform operation S210 described above, and will not be further described here.

[0105] Target risk checkpoint information generation module 520 is configured to input target business model information into the risk point push model and generate target risk checkpoint information related to the target business model information. In one embodiment, target risk checkpoint information generation module 520 can be configured to perform operation S220 described above and will not be further described here.

[0106] The business requirements document acquisition module 530 is used to integrate the target business model information and the target risk checkpoint information to obtain a business requirements document automatically written based on the required design information. In one embodiment, the business requirements document acquisition module 530 can be used to perform the operation S230 described above, which will not be repeated here.

[0107] According to an embodiment of the present disclosure, the demand design information includes the demand item name and the transformation content, and the target business model information includes the target business architecture information. The target business model information determination module includes a candidate business architecture model determination unit and a first target business architecture model determination unit.

[0108] The candidate business architecture model determination unit is used to analyze the existing business architecture model according to the name of the requirement item, and determine the candidate business architecture model in which the existing domain layer information and the existing value stream layer information in the existing business architecture model match the name of the requirement item. The candidate business architecture model also includes the existing activity layer information and the existing task layer information.

[0109] The first target business architecture model determination unit is used to perform similarity matching between the existing activity layer information and the existing task layer information and the transformation content respectively, determine the first target business architecture model whose similarity is higher than the corresponding first threshold, and obtain the target business architecture information.

[0110] According to an embodiment of the present disclosure, the target business model information determination module further includes a historical architecture analysis result acquisition unit, a similarity matching unit, and a second target business architecture model determination unit.

[0111] The historical architecture analysis result acquisition unit is used to acquire the historical architecture analysis results obtained by analyzing the existing business architecture model, wherein the historical architecture analysis results include historical transformation information.

[0112] The similarity matching unit is used to perform similarity matching on the historical transformation information and the transformation content to obtain target historical transformation information with a similarity higher than a second threshold.

[0113] The second target business architecture model determining unit is used to determine the second target business architecture model based on the existing business architecture model used to analyze and obtain target historical transformation information, and obtain target business architecture information.

[0114] According to an embodiment of the present disclosure, the target business model information includes target business product information and target business market information. The target business model information determination module further includes a target business product information acquisition unit and a target business market information acquisition unit.

[0115] The target business product information obtaining unit is used to determine the basic product information associated with the target activity layer information and the target task layer information from the existing product model according to the target activity layer information and the target task layer information in the target business architecture information, and obtain the target business product information.

[0116] The target business market information obtaining unit is used to determine the third-party information and channel information associated with the target activity layer information and the target task layer information from the existing market model under the condition of obtaining authorization, so as to obtain the target business market information.

[0117] According to an embodiment of the present disclosure, the target business model information also includes target demand background information. The target business model information determination module includes a target demand document matching unit and a target demand background information determination unit.

[0118] The target requirement document matching unit is used to retrieve a target requirement document from the historical requirement document that matches the target business product information and target business market information, as well as the target activity layer information and target task layer information in the target business architecture information.

[0119] The target requirement background information determining unit is used to determine the target requirement background information according to the background information in the target requirement document.

[0120] According to an embodiment of the present disclosure, the target business model information further includes target business process information, and the target business model information determination module includes an adjustment unit.

[0121] The adjustment unit is used to adjust the target activity process information in the target business architecture information according to the transformation content to obtain the target business process information.

[0122] According to an embodiment of the present disclosure, the business requirements document includes a business model part generated based on the target business model information and a risk checkpoint information used to determine the target risk checkpoint information. The risk checkpoint information includes existing business link information, existing applicable business product information, existing business dimension information and existing risk checkpoint information. The existing risk checkpoint information is determined based on at least one of the existing business link information, existing applicable business product information and existing business dimension information.

[0123] According to an embodiment of the present disclosure, the risk point push model is obtained by training the following modules: a candidate risk checkpoint information determination module, a target risk checkpoint information matching module, and an optimization training module.

[0124] The candidate risk checkpoint information determination module is used to determine candidate risk checkpoint information from the risk checklist based on target business link information, target applicable business product information, and target business dimension information related to the target business model information.

[0125] The target risk checkpoint information matching module is used to match the checkpoint keyword information of the candidate risk checkpoint information with the model keyword information of the target business model information, and determine the candidate risk checkpoint information with mutually matching keywords as the target risk checkpoint information.

[0126] The optimization training module is used to, in response to determining that the target risk checkpoint information has been added to the risk check library, use the added risk check library to optimize the risk point push model.

[0127] According to embodiments of the present disclosure, any multiple modules among the target business model information determination module 510, the target risk checkpoint information generation module 520, and the business requirements document acquisition module 530 can be combined into a single module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module. According to embodiments of the present disclosure, at least one of the target business model information determination module 510, the target risk checkpoint information generation module 520, and the business requirements document acquisition module 530 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 in hardware or firmware through any other reasonable means of circuit integration or packaging, or can be implemented in any one of the three implementation methods of software, hardware, and firmware, or any appropriate combination of any of these. Alternatively, at least one of the target business model information determination module 510, the target risk checkpoint information generation module 520 and the business requirements document acquisition module 530 can be at least partially implemented as a computer program module, which can perform corresponding functions when executed.

[0128] Figure 6 A block diagram of an electronic device suitable for implementing the requirement writing method according to an embodiment of the present disclosure is schematically shown.

[0129] like Figure 6 As shown, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage portion 608 into a random access memory (RAM) 603. The processor 601 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 601 may also include onboard memory for caching purposes. The processor 601 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.

[0130] Various programs and data required for the operation of the electronic device 600 are stored in the RAM 603. The processor 601, ROM 602, and RAM 603 are connected to each other via a bus 604. The processor 601 executes the various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 602 and / or RAM 603. It should be noted that the programs may also be stored in one or more memories other than the ROM 602 and RAM 603. The processor 601 may also execute the various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0131] According to an embodiment of the present disclosure, electronic device 600 may further include an input / output (I / O) interface 605, which is also connected to bus 604. Electronic device 600 may also include one or more of the following components connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 608 including a hard disk; and a communication section 609 including a network interface card such as a LAN card or modem. Communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. Removable media 611, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 610 as needed, so that computer programs read from the removable media can be installed into storage section 608 as needed.

[0132] 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 and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the requirement-writing method according to the embodiments of the present disclosure.

[0133] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 602 and / or RAM 603 described above, and / or one or more memories other than ROM 602 and RAM 603.

[0134] The embodiments of the present disclosure also include a computer program product, which includes a computer program containing 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 requirements writing method provided by the embodiments of the present disclosure.

[0135] The computer program executes the above functions defined in the system / device of the embodiment of the present disclosure when the processor 601 executes the computer program. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0136] In one embodiment, the computer program may be stored on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may be transmitted and distributed in the form of a signal on a network medium, downloaded and installed via the communication portion 609, and / or installed from a removable medium 611. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to wireless, wired, or any suitable combination thereof.

[0137] In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 609, and / or installed from a removable medium 611. When the computer program is executed by the processor 601, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the systems, devices, means, modules, units, etc. described above can be implemented by computer program modules.

[0138] 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 computer programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C" or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving 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 via the Internet).

[0139] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation 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 flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0140] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0141] The above describes the embodiments of the present disclosure. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for writing requirements, characterized in that: The method comprises: In response to receiving requirement design information related to a business requirement to be written, determining target business model information related to the requirement design information from existing business model information based on the requirement design information; Inputting the target business model information into a risk point push model to generate target risk checkpoint information related to the target business model information; and The target business model information and the target risk checkpoint information are integrated to obtain a business requirement document automatically written according to the demand design information.

2. The method according to claim 1, characterized in that The demand design information includes a demand item name and modification content, and the target business model information includes target business architecture information; determining the target business model information related to the demand design information from the existing business model information based on the demand design information includes: Analyzing existing business architecture models based on the requirement item name to determine a candidate business architecture model in which both existing domain layer information and existing value stream layer information in the existing business architecture model match the requirement item name, and the candidate business architecture model also includes existing activity layer information and existing task layer information; and The existing activity layer information and the existing task layer information are respectively matched with the transformation content in terms of similarity, a first target business architecture model having a similarity higher than a corresponding first threshold is determined, and the target business architecture information is obtained.

3. The method according to claim 2, characterized in that The step of determining target business model information related to the demand design information from existing business model information according to the demand design information further includes: Obtaining historical architecture analysis results obtained by analyzing the existing business architecture model, wherein the historical architecture analysis results include historical transformation information; Performing similarity matching on the historical transformation information and the transformation content to obtain target historical transformation information having a similarity higher than a second threshold; and Based on the existing business architecture model used to analyze and obtain the target historical transformation information, a second target business architecture model is determined to obtain the target business architecture information.

4. The method according to claim 2 or 3, characterized in that The target business model information includes target business product information and target business market information; the step of determining target business model information related to the demand design information from existing business model information based on the demand design information further includes: According to the target activity layer information and the target task layer information in the target business architecture information, basic product information associated with the target activity layer information and the target task layer information is determined from an existing product model to obtain the target business product information; and When authorization is obtained, third-party information and channel information associated with the target activity layer information and the target task layer information are determined from an existing market model to obtain the target business market information.

5. The method according to claim 4, characterized in that The target business model information also includes target demand background information; and determining target business model information related to the demand design information from existing business model information based on the demand design information includes: Retrieving a target requirement document from the historical requirement document that matches the target business product information, the target business market information, and the target activity layer information and the target task layer information in the target business architecture information; and The target requirement background information is determined according to the background information in the target requirement document.

6. The method according to claim 2 or 3, characterized in that The target business model information also includes target business process information. The determining, based on the demand design information, target business model information related to the demand design information from the existing business model information includes: According to the transformation content, the target activity process information in the target business architecture information is adjusted to obtain the target business process information.

7. The method according to claim 1, characterized in that The business requirements document includes a business model part generated based on the target business model information and a risk checkpoint information used to determine the target risk checkpoint information. The risk checkpoint information includes existing business link information, existing applicable business product information, existing business dimension information and existing risk checkpoint information. The existing risk checkpoint information is determined based on at least one of the existing business link information, the existing applicable business product information and the existing business dimension information.

8. The method according to claim 7, characterized in that The risk point push model is trained by the following method: Determining candidate risk checkpoint information from the risk check table based on target business link information, target applicable business product information, and target business dimension information related to the target business model information; matching the checkpoint keyword information of the candidate risk checkpoint information with the model keyword information of the target business model information, and determining the candidate risk checkpoint information having mutually matching keywords as the target risk checkpoint information; as well as In response to determining that the target risk checkpoint information has been added to the risk check library, the risk point push model is optimized and trained using the added risk check library.

9. A demand writing device, characterized in that: The device comprises: a target business model information determining module, configured to, in response to receiving requirement design information related to a business requirement to be written, determine target business model information related to the requirement design information from existing business model information based on the requirement design information; a target risk checkpoint information generating module, configured to input the target business model information into a risk point push model and generate target risk checkpoint information related to the target business model information; and The business requirement document obtaining module is used to integrate the target business model information and the target risk checkpoint information to obtain a business requirement document automatically written according to the demand design information.

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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