Supply chain financial service interaction method and system based on combination of real and virtual

Abstract

The application provides a supply chain financial service interaction method and system based on number-real fusion, and belongs to the technical field of supply chain finance. First, an association mapping relationship between a dialogue interaction scene and a supply chain financial service module is established. Based on the association mapping relationship, a supply chain financial service demand description of a user in dialogue interaction is obtained. A resource matching result is generated by matching a supply chain financial resource pool resource. A dialogue interaction response scheme containing service recommendation, process description and interaction guidance is generated according to the resource matching result. The dialogue interaction response scheme is pushed to a user dialogue terminal and feedback is obtained. Based on the feedback, the association mapping relationship and the resource matching result are updated. Therefore, accurate and dynamic docking of resources and demands can be realized, and the efficiency and quality of supply chain financial service interaction are improved.

Description

Technical Field

[0001] This invention relates to the field of supply chain finance service technology, and more specifically, to a supply chain finance service interaction method and system based on data-real integration. Background Technology

[0002] In today's context of deep integration between digitalization and the real economy, supply chain finance, as a key link in promoting the coordinated development of the industrial chain, faces new challenges and opportunities in its service models and interaction efficiency. Traditional supply chain finance service interaction methods have many limitations.

[0003] On the one hand, existing interaction methods often lack a precise grasp of the diverse dialogue scenarios users encounter within the supply chain business process. When users initiate supply chain finance service requests, they may be at different stages of the business, such as procurement, production, and sales, each with its own unique needs and dialogue scenarios. However, traditional methods struggle to effectively link these rich dialogue scenarios with specific supply chain finance service functions, making it difficult for users to quickly and accurately obtain the services they require.

[0004] On the other hand, in terms of resource matching, traditional methods typically employ relatively fixed matching rules, failing to dynamically select suitable resources from the supply chain finance resource pool based on the user's real-time needs. This significantly reduces the accuracy and flexibility of resource matching, making it unsuitable for the complex and ever-changing demands of supply chain finance operations. Moreover, in terms of interactive response, the response solutions generated by traditional methods often lack specificity and guidance, failing to effectively guide users through the service process, resulting in a poor user experience and impacting the overall efficiency and effectiveness of supply chain finance services. Summary of the Invention

[0005] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a supply chain financial service interaction method based on data-real integration, the method comprising:

[0006] Establish a mapping relationship between dialogue interaction scenarios and supply chain finance service modules. The dialogue interaction scenarios include the types of dialogue scenarios initiated by users in the supply chain business process, and the supply chain finance service modules include the service function units corresponding to supply chain finance business.

[0007] Based on the aforementioned association mapping relationship, the description of the supply chain finance service needs input by the user during the dialogue interaction is obtained;

[0008] Based on the aforementioned association mapping relationship, resources in the supply chain finance resource pool are matched with the description of supply chain finance service needs to generate resource matching results;

[0009] Based on the resource matching results, a dialogue and interaction response scheme for supply chain financial services is generated. The dialogue and interaction response scheme includes service recommendation content, process description content, and interaction guidance content for the demand content.

[0010] The dialogue interaction response scheme is pushed to the user's dialogue terminal and the user feedback result is obtained. The association mapping relationship and the resource matching result are updated based on the user feedback result.

[0011] In another aspect, embodiments of the present invention also provide a supply chain financial service interaction system based on data-real integration, including a processor and a machine-readable storage medium. The machine-readable storage medium is connected to the processor. The machine-readable storage medium is used to store programs, instructions, or code. The processor is used to execute the programs, instructions, or code in the machine-readable storage medium to implement the above-described method.

[0012] Based on the above, this embodiment of the invention establishes a mapping relationship between dialogue interaction scenarios and supply chain finance service modules. This connects diverse dialogue scenarios initiated by users in the supply chain business process with specific service function units. Based on this mapping relationship, user demand descriptions are obtained, and resources in the supply chain finance resource pool are further matched. This achieves dynamic and precise matching of resources and demands, significantly improving the accuracy and flexibility of resource matching and better meeting the complex and ever-changing needs of supply chain finance business. A dialogue interaction response scheme, including service recommendations, process descriptions, and interactive guidance, is generated based on the resource matching results. This provides users with comprehensive, detailed, and guiding service guidance, effectively improving the user experience. The dialogue interaction response scheme is pushed to the user's dialogue terminal, and feedback results are obtained. The mapping relationship and resource matching results are then updated based on the feedback, enabling the entire supply chain finance service interaction process to continuously adapt to market changes and user needs, significantly improving the overall efficiency and quality of supply chain finance services. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the execution flow of the supply chain financial service interaction method based on data-real integration provided in the embodiments of the present invention.

[0014] Figure 2 This is a schematic diagram of exemplary hardware and software components of the supply chain finance service interaction system based on data-real integration provided in the embodiments of the present invention. Detailed Implementation

[0015] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a supply chain finance service interaction method based on data-real integration, provided in one embodiment of the present invention. The following is a detailed description of this supply chain finance service interaction method based on data-real integration.

[0016] Step S110: Establish the association mapping relationship between the dialogue interaction scenario and the supply chain finance service module. The dialogue interaction scenario includes the dialogue scenario types initiated by the user in the supply chain business process, and the supply chain finance service module includes the service function units corresponding to the supply chain finance business.

[0017] In this embodiment, establishing a mapping relationship between dialogue interaction scenarios and supply chain finance service modules is a fundamental and crucial step in the data-driven supply chain finance service interaction system. Dialogue interaction scenarios refer to various scenarios in which users initiate dialogues throughout the entire supply chain business process. For example, in the raw material procurement stage, users might initiate dialogues about procurement financing; in the product sales stage, they might initiate dialogues about accounts receivable management. Supply chain finance service modules, on the other hand, encompass various service function units corresponding to supply chain finance business, such as loan application services, accounts receivable collection services, and risk assessment services. By establishing a mapping relationship between these two, users' dialogue needs can be more accurately matched with appropriate supply chain finance services, improving the efficiency and accuracy of service interaction.

[0018] Step S111: Determine the scene characteristics of the dialogue interaction scenario, wherein the scene characteristics include the supply chain business stage at the time of dialogue initiation, user identity type, and dialogue triggering event.

[0019] When establishing a mapping relationship, the first step is to determine the characteristics of the dialogue interaction scenario. Specifically, these characteristics include the supply chain business stage at the time the dialogue is initiated, the user's identity type, and the dialogue triggering event. Different supply chain business stages generate different financial service needs. For example, in the production stage of the supply chain, companies may need funds to purchase production equipment and raw materials, thus focusing more on production financing services; while in the sales stage, companies may be more concerned with the timely collection and management of accounts receivable. User identity type is also an important scenario characteristic; users with different identities have different needs and permissions in supply chain financial services. For example, suppliers may be more concerned with accounts receivable financing and rapid settlement, while buyers may be more concerned with the loan amount and interest rate for procurement funds. The dialogue triggering event is the specific reason that prompts the user to initiate the dialogue, which may be a risk warning from the system, the arrival of a key node in the business process, or the user's own proactive demand. For example, when the system detects that a payment is about to be overdue, it may trigger a supplier to initiate a dialogue about payment collection.

[0020] Step S1111: Obtain business operation records within a preset time period before the user initiates the dialogue. The business operation records include the user's operation log in the supply chain business system. The operation log includes the operation time, operation module, and operation content.

[0021] To determine the supply chain business stage at the time the conversation is initiated, it's necessary to obtain the user's business operation records within a preset time period prior to the conversation. This preset time period can be set according to actual business conditions, such as the past week or month. The business operation records are stored in the supply chain business system's operation log, which details the user's actions, including the time, module, and content of each action. By analyzing these operation records, we can understand the user's business activity trajectory before the conversation was initiated, thereby inferring the current supply chain business stage. For example, if the operation records show that the user has frequently performed raw material procurement operations recently, then it can be determined that the user is currently in the procurement stage of the supply chain.

[0022] Step S1112: Classify the operation modules and operation content in the operation log, identify the core operations related to the supply chain business process, and determine the supply chain business stage based on the business process nodes corresponding to the core operations.

[0023] After obtaining the business operation records, it is necessary to categorize the operation modules and operation content in the operation logs. Operation modules may include procurement, production, and sales modules, while operation content refers to specific business operations such as placing orders, shipping goods, and receiving payments. By categorizing these operations, core operations related to the supply chain business process can be identified. Core operations are those that play a crucial role in advancing the business process, such as confirming purchase orders and issuing production tasks. Based on the business process nodes corresponding to these core operations, the current stage of the supply chain business can be accurately determined. For example, if the core operations are the completion of production tasks and the receipt of products into inventory, then it can be determined that the current stage is the end of the production phase of the supply chain.

[0024] Step S1113: Extract the user's registration information in the supply chain business system. The registration information includes the user's company type, company size, and business area. Combined with the user's role in the supply chain business, determine the user's identity type.

[0025] Determining a user's identity type requires extracting their registration information from the supply chain business system. This registration information includes the user's company type (e.g., manufacturing, trading); company size (e.g., large, medium, small); and business sector (e.g., electronics, apparel). It also requires considering the user's role in the supply chain, such as supplier, buyer, or logistics provider. Combining this information allows for an accurate determination of the user's identity type. For example, a small electronics manufacturing company participating in the supply chain as a supplier would be classified as a small electronics manufacturing supplier.

[0026] Step S1114: Monitor the real-time operation behavior when the user initiates a dialogue. The real-time operation behavior includes the user's click operation at the dialogue entry point, text input operation in the input box, or selection operation of the menu option. Record the interface elements and operation sequence when the operation behavior occurs.

[0027] To determine the triggering event for a conversation, it's necessary to monitor the user's real-time actions when initiating the conversation. These actions include clicking on the conversation entry point, entering text in input boxes, and selecting menu options. By recording the interface elements and the sequence of these actions, the specific reason for the user initiating the conversation can be analyzed. For example, if a user clicks on the conversation entry point and enters a question about debt collection after seeing a system pop-up notification of overdue payments, then the conversation can be determined that the overdue payment notification triggered the conversation.

[0028] Step S1115: Analyze the functions and operation sequences of the interface elements to determine the triggering event as either the triggering type corresponding to the function entry or the triggering type corresponding to the requirement statement, and generate the dialogue triggering event.

[0029] After recording users' real-time actions, it's necessary to analyze the functions of the interface elements and the intent behind the operational sequence. Based on the analysis results, determine whether the dialogue trigger event is a trigger type corresponding to a function entry point or a trigger type corresponding to a request statement. Trigger types corresponding to function entry points are usually initiated when the user clicks a specific function entry point provided by the system, such as clicking the "Loan Application" button; trigger types corresponding to request statements are initiated when the user initiates a dialogue by inputting a relevant description based on their actual needs, such as a user entering "I need funds to expand production." Through this method, dialogue trigger events can be accurately generated.

[0030] Step S112: Determine the service characteristics of the supply chain finance service module. The service characteristics include the business scope, applicable objects and triggering conditions corresponding to the service function units.

[0031] In addition to determining the scenario characteristics of the dialogue interaction, it is also necessary to determine the service characteristics of the supply chain finance service module. Service characteristics include the business scope, applicable users, and triggering conditions corresponding to the service functional unit. The business scope of the service functional unit clarifies the business areas covered by the service; for example, loan application services may cover production financing, procurement financing, etc.; accounts receivable collection services may cover overdue collection of accounts receivable, payment term management, etc. Applicable users specify which types of users the service is suitable for, such as large enterprises, small enterprises, suppliers, and purchasers. Triggering conditions refer to the conditions required to activate the service functional unit, which may include specific states of business data, user actions, etc. For example, the triggering conditions for a loan application service might be that the enterprise's credit rating reaches a certain standard and there is a clear purpose for the funding need.

[0032] Step S1121: Obtain the business description document for each service function unit in the supply chain finance service module. The business description document includes the functional introduction, service content and application scenario description of the service function unit.

[0033] To determine the business scope of each service functional unit, it is necessary to obtain the business description document for each service functional unit within the supply chain finance service module. The business description document details the functional introduction, service content, and application scenarios of each service functional unit. By reading these documents, one can understand which specific business areas each service functional unit covers. For example, a business description document for a loan application service might detail the types of loans that the service can offer to businesses, the loan amount range, and the applicable business scenarios, thus clarifying its business scope.

[0034] Step S1122: Parse the business specification document to extract descriptions of the business areas covered by the service, categorize the extracted descriptions into a business scope list according to the business areas, and determine the business scope.

[0035] After obtaining the business specification document, it needs to be parsed. Through text parsing, descriptions of the business areas covered by the service are extracted. These descriptions are then categorized and organized according to business areas to form a business scope list. For example, for loan application services, descriptions such as "production financing," "procurement financing," and "equipment purchase financing" might be extracted and added to the business scope list, thereby determining the business scope of this service functional unit.

[0036] Step S1123: Access the permission management system of the supply chain finance service module to obtain the usage permission setting information of each service function unit. The permission setting information includes the user type allowed to use the service function unit, enterprise qualification requirements, and business cooperation period requirements.

[0037] To determine the applicable users of a service function unit, access to the supply chain finance service module's access control system is required. This system stores access permission settings for each service function unit, including the types of users allowed to use it, enterprise qualification requirements, and required years of business cooperation. For example, a large loan application service might require users to be large enterprises with good credit ratings and a certain length of business cooperation. By obtaining this access permission information, the applicable user scope for that service function unit can be clearly defined.

[0038] Step S1124: Filter the permission setting information, extract the access conditions directly related to user access, organize the user types and enterprise types that meet the access conditions into an applicable object list by category, and determine the applicable objects.

[0039] After obtaining the permission settings information, it is necessary to filter it and extract the access conditions directly related to user access. Then, the user types and enterprise types that meet these access conditions are organized by category to form an applicable list. For example, for a certain supply chain finance service function unit, the access conditions might be that the enterprise is a manufacturing company and its annual sales reach a certain amount. Based on these conditions, the qualified enterprise types and user types are filtered out and organized into the applicable list, thereby determining the applicable users of this service function unit.

[0040] Step S1125: Obtain the activation rule document of the service function unit. The activation rule document includes the prerequisite business conditions, user operation requirements and system status requirements required for the service function unit to start.

[0041] To determine the triggering conditions for a service function unit, it is necessary to obtain the activation rule document for that service function unit. The activation rule document details the prerequisite business conditions, user operation requirements, and system status requirements that must be met for the service function unit to start. For example, the activation rule document for a loan application service might stipulate that the company's financial statements must be audited, the user must submit complete loan application materials in the system, and the system's loan quota management module must be in a normal state before the service can be started.

[0042] Step S1126: Decompose the prerequisite business conditions in the activation rule document, sort out the specific operation steps that the user needs to perform, confirm the operating status standard of the supply chain business system, and determine the triggering conditions by comprehensively considering the prerequisite business conditions, user operation requirements and system status requirements.

[0043] After obtaining the activation rules document, the prerequisite business conditions need to be broken down. This breakdown identifies the specific operational steps users need to take, and confirms the operational status standards of the supply chain business system. Taking into account the prerequisite business conditions, user operation requirements, and system status requirements, the triggering conditions for service function units are determined. For example, for accounts receivable collection services, the prerequisite business condition might be that the accounts receivable are overdue for a certain number of days, the user operation requirement is to submit a collection request in the system, and the system status requirement is that the accounts receivable management module is running normally. Only when all these conditions are met can the accounts receivable collection service be triggered.

[0044] Step S113: Based on the overlap between the supply chain business stage in the scenario features and the business scope in the service features, obtain the first association parameter; based on the matching degree between the user identity type in the scenario features and the applicable object in the service features, obtain the second association parameter; based on the fit between the dialogue triggering event in the scenario features and the triggering condition in the service features, obtain the third association parameter.

[0045] After determining the scenario characteristics of the dialogue interaction scenario and the service characteristics of the supply chain finance service module, correlation parameters can be calculated based on these characteristics. Specifically, the first correlation parameter is obtained based on the degree of overlap between the supply chain business stage in the scenario characteristics and the business scope in the service characteristics. The greater the overlap between the supply chain business stage and the service business scope, the higher the value of the first correlation parameter. For example, in the procurement stage, if the business scope of the loan application service covers procurement financing, then the overlap between the two is high, and the value of the first correlation parameter will also be correspondingly high. The second correlation parameter is obtained based on the matching degree between the user identity type in the scenario characteristics and the applicable object in the service characteristics. If the user identity type and the applicable object of the service match perfectly, the second correlation parameter is full; if they match partially, a corresponding value is assigned according to the degree of matching; if they do not match, the second correlation parameter is zero. For example, if a user with the identity of a supplier has a high matching degree with the applicable object of the accounts receivable collection service, the value of the second correlation parameter will be high. The third correlation parameter is obtained based on the degree of fit between the dialogue triggering event in the scenario characteristics and the triggering condition in the service characteristics. If the dialogue trigger event and the service trigger condition are perfectly matched, the third correlation parameter will receive a perfect score; if they are partially matched, a value will be assigned based on the degree of match; if they are not matched at all, the third correlation parameter will be zero. For example, if a user initiates a dialogue due to an overdue payment reminder, the match with the payment collection service trigger condition is high, and the value of the third correlation parameter will also be high.

[0046] Step S114: After standardizing and transforming the first association parameter, the second association parameter, and the third association parameter, weight and combine them to obtain the comprehensive association score between each dialogue interaction scenario and each supply chain finance service module.

[0047] After obtaining the first, second, and third correlation parameters, these parameters need to be standardized. The purpose of standardization is to unify parameters with different dimensions to the same scale for subsequent weighted combination. Common methods for standardization include Z-score standardization and Min-Max standardization. After standardization, each parameter is assigned a corresponding weight, which can be set according to actual business needs and importance. For example, in some business scenarios, the overlap between the supply chain business stage and the service business scope may be more important, so a higher weight can be assigned to the first correlation parameter. The standardized parameters are multiplied by their corresponding weights, and then the results are summed to obtain the comprehensive correlation score between each dialogue interaction scenario and each supply chain finance service module. The higher the comprehensive correlation score, the stronger the correlation between the dialogue interaction scenario and the supply chain finance service module.

[0048] Step S115: Based on the comprehensive correlation score, select the combination pairs of dialogue interaction scenarios and supply chain finance service modules that meet the preset threshold. Organize the correspondence between scenario type and service function unit in each combination pair into structured data to generate the correlation mapping relationship. The structured data includes scenario identifier, service identifier and correlation level corresponding to the comprehensive correlation score.

[0049] Based on the calculated comprehensive correlation score, pairs of dialogue interaction scenarios and supply chain finance service modules that meet a preset threshold are selected. The preset threshold can be set according to actual business conditions, such as a specific score value. Only pairs of scenarios with a comprehensive correlation score that reaches or exceeds this threshold are selected. The correspondence between scenario types and service function units in each selected pair is organized into structured data. This structured data includes scenario identifiers, service identifiers, and the correlation level corresponding to the comprehensive correlation score. The correlation level can be divided according to the range of the comprehensive correlation score; for example, a comprehensive correlation score above 80 is considered a high correlation level, 60-80 is a medium correlation level, and below 60 is a low correlation level. Through this method, the correlation mapping relationship between dialogue interaction scenarios and supply chain finance service modules is generated.

[0050] Step S120: Based on the aforementioned association mapping relationship, obtain the description of the supply chain financial service needs input by the user during the dialogue interaction.

[0051] After establishing the mapping relationship between the dialogue interaction scenario and the supply chain finance service module, the system can obtain the supply chain finance service demand description entered by the user during the dialogue interaction. When the user interacts with the system, they can input relevant information through text, speech-to-text conversion, or menu selection. This information needs to be accurately identified and extracted based on the mapping relationship to form a complete supply chain finance service demand description.

[0052] Step S121: Obtain the initial dialogue content input by the user on the dialogue interaction terminal. The initial dialogue content includes input information generated by the user through text, speech-to-text conversion, or menu selection.

[0053] First, it's necessary to obtain the initial dialogue content input by the user on the interactive terminal. Users can input information in various ways, including directly typing text, using voice input and then converting it to text, or generating input information through menu selection. For example, a user might type "I want to apply for a loan to expand production, with a term of one year," which is the initial dialogue content input through text; or they might input "I need funds to purchase raw materials" through voice, which can be converted to text as the initial dialogue content; or the user could select options such as "Loan Application" or "Production Financing" from the menu provided by the system.

[0054] Step S122: Based on the scene identifier in the association mapping relationship, determine the scene features corresponding to the current dialogue interaction scene, and construct a demand parsing framework according to the supply chain business stage, user identity type and dialogue triggering event in the scene features.

[0055] Next, based on the scene identifiers in the association mapping relationship, the scene features corresponding to the current dialogue interaction scene are determined. Based on the supply chain business stage, user identity type, and dialogue triggering event in the scene features, a demand parsing framework is constructed. The demand parsing framework is a structure used to analyze and understand user needs, which helps the system more accurately extract and organize user input information. For example, in the procurement stage, a dialogue initiated by a supplier regarding funding needs might focus on information such as the amount, term, and purpose of the procurement funds; while in the sales stage, a dialogue initiated by a buyer regarding accounts receivable management might focus on information such as the settlement method and payment terms.

[0056] Step S123: Input the initial dialogue content into the requirement parsing framework, and extract keywords related to financing, accounts receivable management or risk assessment from the initial dialogue content. The keywords include business terms, requirement expression terms and constraint terms.

[0057] The initial dialogue content is input into the constructed requirements analysis framework to extract keywords related to financing, accounts receivable management, or risk assessment. Keywords include business terms, requirements expression terms, and constraint terms. Business terms are professional terms related to supply chain finance, such as "loan amount," "accounts receivable," and "credit rating." Requirements expression terms describe the user's specific needs, such as "apply," "need," and "expand." Constraint terms restrict the needs, such as "term is one year" and "interest rate does not exceed a certain percentage." For example, in the user's input, "I want to apply for a loan to expand production scale, with a term of one year," "loan" is a business term, "apply" is a requirements expression term, and "term is one year" is a constraint term.

[0058] Step S1231: Based on the scenario characteristics in the demand analysis framework and combined with the core areas of supply chain finance business, construct a keyword identification dictionary. The keyword identification dictionary includes a set of business terms, a set of demand expression terms, and a set of constraint terms related to supply chain finance business. The set of business terms includes terms related to financing, terms related to accounts receivable management, and terms related to risk assessment. The set of demand expression terms includes terms indicating demand actions and terms indicating demand priority or time requirements. The set of constraint terms includes terms indicating demand constraint elements.

[0059] To extract keywords more accurately, a keyword identification dictionary needs to be constructed based on the scenario characteristics within the demand analysis framework and the core areas of supply chain finance. This keyword identification dictionary includes a set of business terms, a set of demand expression terms, and a set of constraint terms. The business terms set is further divided into financing-related terms, accounts receivable management-related terms, and risk assessment-related terms. For example, financing-related terms might include "loan," "financing," and "credit line"; accounts receivable management-related terms might include "accounts receivable," "payment period," and "collection"; and risk assessment-related terms might include "credit rating" and "risk coefficient." The demand expression term set includes terms indicating demand actions, such as "application," "inquiry," and "adjustment," as well as terms indicating demand priority or time requirements, such as "urgent," "as soon as possible," and "long-term." The constraint term set includes terms indicating demand constraints, such as "amount limit," "interest rate range," and "term limit."

[0060] Step S1232: Split the initial dialogue content into sentences to obtain multiple dialogue sentences, and perform word segmentation on each dialogue sentence to obtain multiple word segmentation units.

[0061] After constructing the keyword recognition dictionary, the initial dialogue content is split into sentences, resulting in multiple dialogue sentences. Each dialogue sentence is then segmented into multiple word units. Word segmentation can employ common natural language processing techniques, such as rule-based segmentation and statistical segmentation methods. For example, the sentence "I want to apply for a loan to expand production scale" will be segmented into word units such as "I," "want," "apply," "amount," "loan," "for," "expand," and "production scale."

[0062] Step S1233: Compare each word segmentation unit with the terms and words in the keyword recognition dictionary. If the word segmentation unit belongs to the business term set, the requirement expression word set, or the constraint condition word set, then mark the word segmentation unit as a keyword. Collect all marked word segmentation units to obtain the keyword.

[0063] Each segmented unit obtained from the word segmentation process is compared with the terms and words in the keyword identification dictionary. If a segmented unit belongs to the set of business terms, the set of demand description terms, or the set of constraint terms, then the segmented unit is marked as a keyword. For example, in the above segmented units, "application" belongs to the demand description term set, and "loan" belongs to the business term set, so they are marked as keywords. Finally, all marked segmented units are collected to obtain the complete keyword list.

[0064] Step S124: Perform semantic association analysis on the extracted keywords to determine the logical relationships between the keywords, and organize the keywords into structured requirement statements based on the logical relationships. The logical relationships include causal relationships, conditional relationships, and parallel relationships.

[0065] After extracting the keywords, semantic association analysis is needed. This analysis determines the logical relationships between the keywords, such as causal, conditional, or parallel relationships. For example, in the keywords "apply," "loan," and "expand production scale," the purpose of "applying for a loan" is to "expand production scale," which is a causal relationship. Based on these logical relationships, the keywords are organized into structured requirement statements. Structured requirement statements can more clearly express the user's needs; for example, the above keywords could be organized as "Apply for a loan to expand production scale."

[0066] Step S125: If key information is missing from the structured demand statement, a supplementary inquiry is initiated to the user through the dialogue interaction terminal to obtain the supplementary input information from the user. The supplementary input information is then integrated into the structured demand statement to generate the supply chain financial service demand description.

[0067] After generating the structured requirement statement, check for any missing key information. If key information is missing, supplementary questions can be posed to the user through the interactive terminal. For example, if the structured requirement statement only mentions "applying for a loan" but doesn't specify the loan amount or term, the user can be asked, "What is the loan amount you are applying for? What is the term?" After obtaining the supplementary information from the user, it is integrated into the structured requirement statement to generate a complete supply chain finance service requirement description. For example, if the user supplements the requirement with "loan amount of one million yuan, term of two years," integrating this information into the structured requirement statement yields "To expand production scale, I am applying for a loan of one million yuan with a term of two years," which is the complete supply chain finance service requirement description.

[0068] Step S130: Based on the association mapping relationship, match the resources in the supply chain finance resource pool with the supply chain finance service demand description to generate resource matching results.

[0069] After obtaining the user's description of their supply chain finance service needs, it is necessary to match the resources in the supply chain finance resource pool with the description of the needs based on the previously established correlation mapping relationship, thereby generating resource matching results. The supply chain finance resource pool contains various financial resources, such as loan products and accounts receivable management services provided by different financial institutions. By matching, the most suitable resources for the user's needs can be identified.

[0070] Step S131: Extract resource characteristics from the supply chain finance resource pool. The resource characteristics include the qualification information of institutional resources, the service terms of product resources, and the execution steps of process resources. The qualification information includes the business license scope and cooperation level of the institution. The service terms include the interest rate standard and term requirements of the product. The execution steps include the processing steps and required materials of the process.

[0071] First, it's necessary to extract the resource characteristics from the supply chain finance resource pool. These characteristics include the qualification information of institutional resources, the service terms of product resources, and the execution steps of process resources. Institutional qualification information primarily includes the institution's business license scope and cooperation level. The business license scope specifies the types of financial business the institution can conduct, while the cooperation level reflects the depth and stability of the institution's cooperation with the supply chain finance system. The service terms of product resources include important information such as the product's interest rate and term requirements; for example, a loan product might have an annual interest rate of 5% and a term of three years. The execution steps of process resources detail the necessary steps and materials for handling related business. For example, a loan application process might include submitting application materials, reviewing materials, and signing a contract; required materials might include the company's business license and financial statements.

[0072] Step S132: Based on the service identifier in the association mapping relationship, determine the service function unit corresponding to the supply chain finance service demand description, and extract the service attributes of the service function unit. The service attributes include the resource type requirements and resource parameter requirements corresponding to the service.

[0073] Based on the service identifiers in the association mapping relationship, the service functional units corresponding to the supply chain finance service demand descriptions are identified. Each service functional unit has its specific service attributes, including resource type requirements and resource parameter requirements. Resource type requirements clarify which type of resources are needed to meet the demand; for example, a loan application service may require loan product resources provided by a financial institution, while a debt collection service may require service resources from a debt management institution. Resource parameter requirements specify the specific parameter indicators of the resources, such as the loan amount range and interest rate range of the loan product. For example, for the demand description of "applying for a loan of one million yuan with a term of two years," the corresponding service functional unit is the loan application service, its resource type requirement is loan product resources, and its resource parameter requirements are a loan amount of one million yuan and a term of two years.

[0074] Step S133: Based on the demand content in the supply chain finance service demand description, select candidate resource features corresponding to the demand content from the resource features. The candidate resource features must meet the resource type requirements in the service attributes.

[0075] Based on the demand details in the supply chain finance service demand description, candidate resource features corresponding to the demand content are selected from the extracted resource features. These candidate resource features must meet the resource type requirements in the service attributes. For example, for a loan application demand, all loan product resources can be selected from the resource features; these loan product resources are the candidate resource features. Further screening is needed to ensure that these candidate resource features can, to some extent, meet other requirements of the demand content, such as loan amount and term.

[0076] Step S134: Perform attribute matching analysis on the candidate resource features and the demand content in the supply chain finance service demand description. Compare the qualification information, service terms and execution steps in the candidate resource features with the corresponding demand points in the demand content one by one to determine the matching degree of each candidate resource feature.

[0077] After identifying candidate resource features, attribute matching analysis is needed to compare these features with the requirements in the supply chain finance service demand description. Specifically, the qualification information, service terms, and execution steps in the candidate resource features are compared one by one with the corresponding requirements in the demand description. For example, for loan product resources, their interest rate standards and term requirements are compared with the interest rate and term requirements in the demand description; the institution's business license scope is compared with the business requirements in the demand description. Through comparison, the matching degree of each candidate resource feature is determined. The higher the matching degree, the more suitable the candidate resource feature is for the user's needs.

[0078] Step S1341: Break down the demand content in the supply chain finance service demand description into multiple demand points. The demand points include the amount demand, term demand, and interest rate demand in the financing demand; the payment period inquiry demand and payment settlement demand in the accounts receivable management demand; and the credit rating demand and risk screening demand in the risk assessment demand.

[0079] To conduct more accurate attribute matching analysis, the requirements in the supply chain finance service demand description need to be broken down into multiple demand points. Different types of demands contain different demand points. For example, financing needs may include amount requirements, term requirements, and interest rate requirements; accounts receivable management needs may include accounts receivable inquiry and accounts receivable settlement requirements; risk assessment needs may include credit rating requirements and risk screening requirements. For example, for a demand description of "applying for a loan of one million yuan, with a term of two years and an interest rate not exceeding five percent", the demand points include the amount requirement (one million yuan), the term requirement (two years), and the interest rate requirement (not exceeding five percent).

[0080] Step S1342: Set a corresponding weight value for each requirement point. The weight value of core requirement points is higher than that of non-core requirement points. The core requirement points are those that the user explicitly emphasizes or that play a key role in the development of the business.

[0081] After identifying the demand points, a corresponding weight value needs to be assigned to each demand point. The weight value of core demand points should be higher than that of non-core demand points. Core demand points are those explicitly emphasized by the user or those that play a crucial role in business operations. For example, in a loan application, the amount and term requirements are usually core demand points because they directly relate to whether the user can obtain the necessary financial support; while the interest rate requirement may be relatively non-core. Let's assume a weight value of 0.4 for the amount requirement, 0.3 for the term requirement, and 0.2 for the interest rate requirement.

[0082] Step S1343: For each candidate resource feature, extract its qualification information, service terms, and attribute information corresponding to each requirement point in the execution steps. If the business license scope in the qualification information covers the scope corresponding to the amount requirement, then the attribute information matches the amount requirement point; if the term requirement in the service terms meets the scope corresponding to the term requirement, then the attribute information matches the term requirement point; if the processing steps in the execution steps meet the process corresponding to the payment settlement requirement, then the attribute information matches the payment settlement requirement point.

[0083] For each candidate resource feature, extract its qualification information, terms of service, and attribute information corresponding to each requirement point in the execution steps. Then determine whether these attribute information matches the requirement point. For example, for a monetary requirement point, if the business license scope in the qualification information of the candidate resource feature covers the monetary range in the requirement description, then the attribute information is considered to match the monetary requirement point; for a time limit requirement point, if the time limit requirement in the terms of service meets the time limit range in the requirement description, then the attribute information is considered to match the time limit requirement point.

[0084] Step S1344: Set a matching score for each requirement point. Requirement points that match completely are given full marks, requirement points that match partially are given corresponding scores according to the degree of matching, and requirement points that do not match are given zero marks.

[0085] Assign a matching score to each demand point. A perfect match earns full marks; a partial match earns a score based on the degree of match; and a no-match earns zero marks. For example, for a monetary demand point, if the loan amount of the candidate resource feature is exactly equal to the amount in the demand description, a full score of 10 is awarded; if the loan amount is within a certain range of the demand amount but not exactly equal, a score of 6-9 is awarded based on the degree of deviation; and if the loan amount is outside the range of the demand amount, zero marks are awarded.

[0086] Step S1345: Multiply the matching score of each demand point by the corresponding weight value to obtain the weighted score of each demand point. Add the weighted scores of all demand points to obtain the total matching score of the candidate resource feature. Convert the total matching score into a percentage form to obtain the matching compliance degree.

[0087] The matching score for each demand point is multiplied by its corresponding weight value to obtain a weighted score for each demand point. For example, if the matching score for the monetary demand point is 8 points and the weight value is 0.4, then the weighted score for the monetary demand point is 8 × 0.4 = 3.2 points. The weighted scores of all demand points are summed to obtain the total matching score for the candidate resource feature. Finally, the total matching score is converted to a percentage to obtain the matching degree. For example, a total matching score of 7 points translates to 70% in percentage form, which is the matching degree for the candidate resource feature.

[0088] Step S135: Sort the candidate resource features according to the matching degree, filter out the candidate resource features whose matching degree exceeds a preset threshold, organize the institutional resources, product resources and process resources corresponding to the filtered candidate resource features into a resource list, the resource list includes resource name, resource feature and matching degree, and generate the resource matching result.

[0089] Candidate resource features are sorted based on the calculated matching scores, with features showing higher scores ranked higher. Then, candidate resource features with matching scores exceeding a preset threshold are selected. This preset threshold can be set according to actual business needs, for example, 60%. The institutional resources, product resources, and process resources corresponding to the selected candidate resource features are compiled into a resource list, which includes the resource name, resource features, and matching score. For example, the resource list might include a bank's loan product, whose resource features include interest rate standards and term requirements, with a matching score of 80%. Resource matching results are generated through this process.

[0090] Step S140: Generate a dialogue and interaction response plan for supply chain financial services based on the resource matching results. The dialogue and interaction response plan includes service recommendation content, process description content, and interaction guidance content for the demand content.

[0091] After obtaining the resource matching results, a dialogue and interaction response plan for supply chain finance services needs to be generated based on these results. This plan includes service recommendations tailored to the user's needs, process descriptions, and interactive guidance, providing comprehensive service information and guidance to help users better understand and choose the supply chain finance services that best suit their requirements.

[0092] Step S141: Extract the resource features corresponding to each resource from the resource list of the resource matching results, and convert the qualification information of the institutional resources, the service terms of the product resources, and the execution steps of the process resources into natural language descriptions that users can understand, and generate basic recommendation content.

[0093] First, extract the resource features corresponding to each resource from the resource list in the resource matching results. Transform the qualification information of institutional resources, the service terms of product resources, and the execution steps of process resources into natural language descriptions that users can understand. For example, for a bank's loan product, its resource features include an annual interest rate of 5% and a term of three years, which can be translated into natural language as "This loan product has an annual interest rate of 5% and a loan term of three years." Perform the above transformation on all resource features to generate basic recommendation content, which allows users to gain a preliminary understanding of the basic information of each resource.

[0094] Step S142: Based on the demand content in the supply chain finance service demand description, identify the user's core and secondary demand points. According to the priority of the core and secondary demand points, prioritize the basic recommended content and rank the basic recommended content corresponding to the resource with the highest matching degree with the core demand point at the top to generate service recommended content.

[0095] Based on the demand descriptions in supply chain finance service requests, we identify users' core and secondary needs. Core needs are those that users care about most, while secondary needs are relatively less important. Based on the priority of core and secondary needs, we prioritize the basic recommended content. The basic recommended content corresponding to the resources with the highest match to the core needs is ranked first. For example, in a loan application request, if the user's core needs are loan amount and term, then the basic recommended content for loan products whose loan amount and term best match the demand description will be ranked first, generating service recommendations. Service recommendations highlight the resources that best meet the user's needs, making it easier for users to quickly find suitable services.

[0096] Step S143: Based on the execution steps of the process resources in the resource list, sort out the processing procedures corresponding to each resource, break down the execution steps into multiple process nodes in chronological order, generate process description content, and provide a detailed description of each process node, including the node name, node operation requirements and associated resources.

[0097] Based on the execution steps of the process resources in the resource list, the processing flow for each resource is outlined. The execution steps are broken down into multiple process nodes in chronological order, generating process descriptions. Each process node is described in detail, including its name, operational requirements, and associated resources. For example, for a loan application process, process nodes might include submitting application materials, reviewing materials, and signing a contract. For the "submit application materials" node, the operational requirements might be "prepare business licenses, financial statements, and other materials and upload them to the system," and the associated resource might be the system's document upload module. Through these detailed descriptions, users clearly understand the processing flow for each resource.

[0098] Step S144: Analyze the user's interaction habits during the dialogue interaction process, determine the user's preferred information display method and interaction response speed, and determine the guidance method based on the interaction habits. The guidance method includes button guidance, link guidance, or question and answer guidance.

[0099] Analyze user interaction habits during the dialogue process. By analyzing users' previous operation records and dialogue behavior, determine their preferred information display methods and interaction response speeds. Information display methods may include text display, image and text display, and voice display, while interaction response speeds may be categorized as fast response and normal response. Determine the guidance method based on the user's interaction habits, including button guidance, link guidance, or question-and-answer guidance. For example, if users prefer to quickly obtain information and like direct operation, button guidance may be used, providing buttons such as "Apply Now" and "View Details" on the interface; if users prefer to gradually understand information, question-and-answer guidance may be used, guiding users to explore the service more deeply through questions.

[0100] Step S145: Based on the service recommendation content, process description content, and guidance method, construct a dialogue interaction response framework, place the service recommendation content in the core area of ​​the dialogue interaction response framework, place the process description content in the auxiliary area of ​​the framework, and embed the guidance method into the operation area of ​​the framework in the form of interactive elements to generate the dialogue interaction response scheme.

[0101] Based on service recommendations, process descriptions, and guidance methods, a dialogue interaction response framework is constructed. Service recommendations are placed in the core area of ​​the framework, allowing users to see the most relevant service information at first glance. Process descriptions are placed in the auxiliary area of ​​the framework, making it convenient for users to view detailed procedures when needed. Guidance methods are embedded in the framework's operation area as interactive elements, such as buttons guiding users to buttons or links guiding users to links, facilitating user interaction. Through these methods, a complete dialogue interaction response solution is generated, providing users with a comprehensive and clear service interaction experience.

[0102] Step S150: Push the dialogue interaction response scheme to the user's dialogue terminal and obtain the user feedback result, and update the association mapping relationship and the resource matching result based on the user feedback result.

[0103] After generating the dialogue interaction response plan, it needs to be pushed to the user's dialogue terminal, and the user's feedback needs to be obtained. Based on the user feedback, the association mapping relationship and resource matching results are updated to improve the accuracy and adaptability of service interaction.

[0104] For example, step S151: convert the dialogue interaction response scheme into a display format supported by the user's dialogue terminal, wherein the display format includes text format, graphic and text combination format or voice broadcast format.

[0105] First, the dialogue interaction response scheme is converted into a display format supported by the user's dialogue terminal. The user's dialogue terminal may support different display formats, including text, a combination of text and images, or voice broadcasting. For example, if the user is using a mobile text chat interface, the dialogue interaction response scheme may be converted to text format; if the user is using a terminal with text and image display capabilities, it may be converted to a combination of text and images; if the user is using a voice interaction device, it may be converted to voice broadcasting. By converting the display format, it ensures that users can correctly receive and view the dialogue interaction response scheme on their own terminals.

[0106] Step S152: Push the converted dialogue interaction response scheme to the user's dialogue terminal through the communication interface, monitor the data transmission status during the push process, and re-initiate the push if a transmission interruption occurs.

[0107] The converted dialogue response scheme is pushed to the user's dialogue terminal via a communication interface. During the push process, the data transmission status is monitored to ensure that the data is accurately and completely transmitted to the user terminal. If a transmission interruption occurs, the push can be restarted until the data is successfully transmitted. For example, if an unstable network signal causes a push interruption, it can be automatically detected and the dialogue response scheme can be re-pushed after the network is restored.

[0108] Step S153: Monitor the user's dialogue terminal operation behavior, and extract user feedback information based on the operation behavior, including the user clicking on interactive elements, inputting text feedback, or triggering voice feedback.

[0109] Monitor user actions on the dialogue terminal and extract user feedback based on these actions. User actions include clicking interactive elements, such as clicking the "Apply Now" button or the "View Details" link; inputting text feedback, such as entering evaluations of service recommendations or requesting modifications in the dialogue interface; and triggering voice feedback, such as expressing opinions and needs via voice. By monitoring these actions, user feedback can be extracted to understand users' views and attitudes towards the dialogue interaction response plan.

[0110] Step S154: If the feedback information contains an expression of approval for the dialogue interaction response plan, it is determined as confirmation information; if the feedback information contains a request to adjust the service recommendation content or process description content, it is determined as modification information; if the feedback information contains a new requirement statement, it is determined as supplementary requirement information. The confirmation information, modification information, or supplementary requirement information are integrated to generate the user feedback result.

[0111] The extracted user feedback information is analyzed. If the feedback contains expressions of approval for the dialogue response plan, such as "This plan is very good, I am very satisfied," it is identified as confirmation information. If the feedback contains requests for adjustments to the service recommendations or process descriptions, such as "I hope the loan term can be extended," it is identified as modification information. If the feedback contains new requests, such as "I also need to learn about the risk assessment service," it is identified as supplementary request information. Confirmation information, modification information, and supplementary request information are integrated to generate user feedback results.

[0112] Step S155: If the user feedback results contain modification information, adjust the resource list in the resource matching results according to the modification information and re-filter the candidate resource features; if the user feedback results contain supplementary demand information, integrate the supplementary demand information into the supply chain finance service demand description and re-execute the resource matching steps; adjust the comprehensive association score in the association mapping relationship according to the scenario and service adaptability reflected in the user feedback results.

[0113] The association mapping relationship and resource matching results are updated based on user feedback. If the user feedback includes modification information, the resource list in the resource matching results is adjusted accordingly, and candidate resource features are re-screened. For example, if a user requests an extension of the loan term, loan product resources that meet the new term requirement are re-screened. If the user feedback includes supplementary demand information, this supplementary demand information is integrated into the supply chain finance service demand description, and the resource matching steps are re-executed to find resources that better meet the user's needs. Simultaneously, the comprehensive association score in the association mapping relationship is adjusted based on the suitability of the scenario and service reflected in the user feedback results. If a user is dissatisfied with a service recommendation, it indicates a potential problem with the association between the scenario and the service, and the comprehensive association score is lowered accordingly; if a user is satisfied with a service recommendation, the comprehensive association score is raised. Through these methods, the association mapping relationship and resource matching results are continuously optimized to improve the quality of supply chain finance service interactions.

[0114] Figure 2 The illustration shows exemplary hardware and software components of a data-real fusion-based supply chain finance service interaction system 100, which can implement the ideas of this application, according to some embodiments of this application. For example, a processor 120 can be used in the data-real fusion-based supply chain finance service interaction system 100 and to perform the functions described in this application.

[0115] The data-real fusion-based supply chain finance service interaction system 100 can be a general-purpose server or a special-purpose server; both can be used to implement the data-real fusion-based supply chain finance service interaction method of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the load.

[0116] For example, the data-real fusion-based supply chain finance service interaction system 100 may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and various forms of storage media 140, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the data-real fusion-based supply chain finance service interaction system 100 may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The methods of this application can be implemented according to these program instructions. The data-real fusion-based supply chain finance service interaction system 100 also includes an I / O interface 150 between the computer and other input / output devices.

[0117] For ease of explanation, only one processor is described in the data-real fusion-based supply chain finance service interaction system 100. However, it should be noted that the data-real fusion-based supply chain finance service interaction system 100 of this application may also include multiple processors. Therefore, the steps executed by one processor as described in this application may also be executed jointly or individually by multiple processors. For example, if the processor of the data-real fusion-based supply chain finance service interaction system 100 executes steps A and B, it should be understood that steps A and B may also be executed jointly by two different processors or individually by one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor jointly execute steps A and B.

[0118] Furthermore, this embodiment of the invention also provides a readable storage medium, wherein computer-executable instructions are preset in the readable storage medium, and when the processor executes the computer-executable instructions, the above-mentioned supply chain financial service interaction method based on data-real integration is implemented.

[0119] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A supply chain finance service interaction method based on data-real integration, characterized in that, The method includes: Establish a mapping relationship between dialogue interaction scenarios and supply chain finance service modules. The dialogue interaction scenarios include the types of dialogue scenarios initiated by users in the supply chain business process, and the supply chain finance service modules include the service function units corresponding to supply chain finance business. Based on the aforementioned association mapping relationship, the description of the supply chain finance service needs input by the user during the dialogue interaction is obtained; Based on the aforementioned association mapping relationship, resources in the supply chain finance resource pool are matched with the description of supply chain finance service needs to generate resource matching results; Based on the resource matching results, a dialogue and interaction response scheme for supply chain financial services is generated. The dialogue and interaction response scheme includes service recommendation content, process description content, and interaction guidance content for the demand content. The dialogue interaction response scheme is pushed to the user's dialogue terminal and the user feedback result is obtained. The association mapping relationship and the resource matching result are updated based on the user feedback result. The establishment of the association mapping relationship between the dialogue interaction scenario and the supply chain finance service module includes: Determine the scenario characteristics of the dialogue interaction scenario, which include the supply chain business stage at the time of dialogue initiation, user identity type, and dialogue triggering event; Determine the service characteristics of the supply chain finance service module, wherein the service characteristics include the business scope, applicable objects and triggering conditions corresponding to the service function units; Based on the overlap between the supply chain business stage in the scenario features and the business scope in the service features, a first association parameter is obtained; based on the matching degree between the user identity type in the scenario features and the applicable object in the service features, a second association parameter is obtained; and based on the fit between the dialogue triggering event in the scenario features and the triggering condition in the service features, a third association parameter is obtained. The first, second, and third correlation parameters are standardized and then weighted to obtain a comprehensive correlation score between each dialogue interaction scenario and each supply chain finance service module. Based on the comprehensive correlation score, pairs of dialogue interaction scenarios and supply chain finance service modules that meet the preset threshold are selected. The correspondence between scenario type and service function unit in each pair is organized into structured data to generate the correlation mapping relationship. The structured data includes scenario identifier, service identifier and correlation level corresponding to the comprehensive correlation score.

2. The supply chain finance service interaction method based on data-real integration according to claim 1, characterized in that, The scene features for determining the dialogue interaction scenario include: Obtain business operation records within a preset time period before the user initiates a dialogue. The business operation records include the user's operation log in the supply chain business system. The operation log includes the operation time, operation module, and operation content. The operation modules and operation content in the operation log are classified, the core operations related to the supply chain business process are identified, and the supply chain business stage is determined according to the business process node corresponding to the core operation. Extract user registration information from the supply chain business system. The registration information includes the user's company type, company size, and business area. Combine this with the user's role in the supply chain business to determine the user's identity type. Monitor real-time user actions when a conversation is initiated. These actions include clicking on the conversation entry point, inputting text into an input box, or selecting menu options. Record the interface elements and the order of actions when they occur. Analyze the functions and operation sequences of the interface elements to determine the intent, identify the dialogue trigger event as either the trigger type corresponding to the function entry or the trigger type corresponding to the requirement statement, and generate the dialogue trigger event.

3. The supply chain finance service interaction method based on data-real integration according to claim 1, characterized in that, The service characteristics of the supply chain finance service module include: Obtain the business description document for each service function unit in the supply chain finance service module. The business description document includes the functional introduction, service content and application scenario description of the service function unit. The business specification document is parsed to extract descriptions of the business areas covered by the service. The extracted descriptions are then categorized and organized into a business scope list according to the business areas to determine the business scope. Access the permission management system of the supply chain finance service module to obtain the usage permission setting information for each service function unit. The permission setting information includes the user types allowed to use the service function unit, enterprise qualification requirements, and business cooperation duration requirements. Filter the permission settings information, extract the access conditions directly related to user access, organize the user types and enterprise types that meet the access conditions into an applicable object list by category, and determine the applicable objects; Obtain the activation rule document for the service function unit, which includes the prerequisite business conditions, user operation requirements, and system status requirements required to start the service function unit; The prerequisite business conditions in the activation rules document are broken down to identify the specific operation steps that users need to perform, the operating status standards of the supply chain business system are confirmed, and the triggering conditions are determined by comprehensively considering the prerequisite business conditions, user operation requirements, and system status requirements.

4. The supply chain financial service interaction method based on data-real integration according to claim 1, characterized in that, The process of obtaining the user's supply chain finance service demand description input during the dialogue interaction based on the aforementioned association mapping relationship includes: Acquire the initial dialogue content input by the user on the dialogue interaction terminal, wherein the initial dialogue content includes input information generated by the user through text, speech-to-text conversion or menu selection; Based on the scene identifier in the association mapping relationship, the scene features corresponding to the current dialogue interaction scene are determined, and a requirement parsing framework is constructed according to the supply chain business stage, user identity type and dialogue triggering event in the scene features; Input the initial dialogue content into the requirement parsing framework and extract keywords related to financing, accounts receivable management or risk assessment from the initial dialogue content. The keywords include business terms, requirement expression words and constraint words. Semantic association analysis is performed on the extracted keywords to determine the logical relationships between the keywords. Based on the logical relationships, the keywords are organized into structured requirement statements. The logical relationships include causal relationships, conditional relationships, and parallel relationships. If key information is missing from the structured demand statement, a supplementary inquiry is initiated to the user through the interactive dialogue terminal to obtain the supplementary input information from the user. The supplementary input information is then integrated into the structured demand statement to generate the supply chain financial service demand description.

5. The supply chain financial service interaction method based on data-real integration according to claim 4, characterized in that, The initial dialogue content is input into the requirements analysis framework to extract keywords related to financing, accounts receivable management, or risk assessment from the initial dialogue content, including: Based on the scenario characteristics in the demand analysis framework and combined with the core areas of supply chain finance business, a keyword identification dictionary is constructed. The keyword identification dictionary includes a set of business terms, a set of demand expression terms, and a set of constraint terms related to supply chain finance business. The set of business terms includes terms related to financing, terms related to accounts receivable management, and terms related to risk assessment. The set of demand expression terms includes terms indicating demand actions and terms indicating demand priority or time requirements. The set of constraint terms includes terms indicating demand constraint elements. The initial dialogue content is split into sentences to obtain multiple dialogue sentences. Each dialogue sentence is then segmented into multiple word units. Each word segmentation unit is compared with the terms and words in the keyword recognition dictionary. If the word segmentation unit belongs to the business term set, the requirement expression word set, or the constraint condition word set, then the word segmentation unit is marked as a keyword. All marked word segmentation units are collected to obtain the keyword.

6. The supply chain finance service interaction method based on data-real integration according to claim 1, characterized in that, The process of matching resources in the supply chain finance resource pool with the supply chain finance service demand description based on the association mapping relationship to generate resource matching results includes: Extract resource characteristics from the supply chain finance resource pool. The resource characteristics include the qualification information of institutional resources, the service terms of product resources, and the execution steps of process resources. The qualification information includes the business license scope and cooperation level of the institution. The service terms include the interest rate standard and term requirements of the product. The execution steps include the processing steps and required materials of the process. Based on the service identifier in the association mapping relationship, the service functional unit corresponding to the supply chain finance service demand description is determined, and the service attributes of the service functional unit are extracted. The service attributes include the resource type requirements and resource parameter requirements corresponding to the service. Based on the demand content in the supply chain finance service demand description, candidate resource features corresponding to the demand content are selected from the resource features. The candidate resource features must meet the resource type requirements in the service attributes. Attribute matching analysis is performed on the candidate resource characteristics and the demand content in the supply chain finance service demand description. The qualification information, service terms and execution steps in the candidate resource characteristics are compared with the corresponding demand points in the demand content one by one to determine the matching degree of each candidate resource characteristic. Candidate resource features are sorted according to the matching degree, and candidate resource features with matching degree exceeding a preset threshold are selected. The institutional resources, product resources and process resources corresponding to the selected candidate resource features are organized into a resource list, which includes resource name, resource feature and matching degree, and the resource matching result is generated.

7. The supply chain financial service interaction method based on data-real integration according to claim 6, characterized in that, The attribute matching analysis of candidate resource characteristics and supply chain finance service demand description involves comparing the qualification information, service terms, and execution steps in the candidate resource characteristics with the corresponding demand points in the demand content one by one to determine the matching degree of each candidate resource characteristic, including: The demand content in the supply chain finance service demand description is broken down into multiple demand points, which include the amount demand, term demand, and interest rate demand in financing demand; the account management demand includes the account period inquiry demand and account settlement demand; and the risk assessment demand includes the credit rating demand and risk screening demand. Each requirement is assigned a corresponding weight value. The weight value of core requirement points is higher than that of non-core requirement points. The core requirement points are those that users explicitly emphasize or that play a key role in the development of the business. For each candidate resource feature, extract the attribute information corresponding to each requirement point from its qualification information, service terms, and execution steps. If the business license scope in the qualification information covers the scope corresponding to the amount requirement, then the attribute information matches the amount requirement point; if the term requirement in the service terms meets the scope corresponding to the term requirement, then the attribute information matches the term requirement point; if the processing steps in the execution steps meet the process corresponding to the payment settlement requirement, then the attribute information matches the payment settlement requirement point. Set a matching score for each requirement. Requirements that match completely are given full score, requirements that match partially are given a corresponding score based on the degree of matching, and requirements that do not match are given zero score. Multiply the matching score of each demand point by its corresponding weight value to obtain the weighted score of each demand point. Add the weighted scores of all demand points to obtain the total matching score of the candidate resource feature. Convert the total matching score into a percentage to obtain the matching compliance degree.

8. The supply chain finance service interaction method based on data-real integration according to claim 1, characterized in that, The dialogue and interaction response scheme for generating supply chain financial services based on the resource matching results includes: Extract the resource features corresponding to each resource from the resource list of the resource matching results, and transform the qualification information of the institutional resources, the service terms of the product resources, and the execution steps of the process resources into natural language descriptions that users can understand, and generate basic recommendation content. Based on the demand content in the supply chain finance service demand description, the user's core and secondary demand points are identified. According to the priority of the core and secondary demand points, the basic recommended content is prioritized and ranked. The basic recommended content corresponding to the resource with the highest matching degree with the core demand point is ranked first, and service recommended content is generated. Based on the execution steps of the process resources in the resource list, sort out the processing procedures corresponding to each resource, break down the execution steps into multiple process nodes in chronological order, generate process descriptions, and provide detailed descriptions of each process node, including the node name, node operation requirements, and associated resources. Analyze users' interaction habits during the dialogue process, determine users' preferred information display methods and interaction response speeds, and determine the guidance methods based on the interaction habits. The guidance methods include button guidance, link guidance, or question-and-answer guidance. Based on the service recommendation content, process description content, and guidance method, a dialogue interaction response framework is constructed. The service recommendation content is placed in the core area of ​​the dialogue interaction response framework, the process description content is placed in the auxiliary area of ​​the framework, and the guidance method is embedded in the operation area of ​​the framework in the form of interactive elements to generate the dialogue interaction response scheme.

9. A supply chain finance service interaction system based on data-real integration, characterized in that, The system includes a processor and a memory, the memory and the processor being connected. The memory is used to store programs, instructions or code, and the processor is used to execute the programs, instructions or code in the memory to implement the supply chain financial service interaction method based on data-real integration as described in any one of claims 1-8.

Images