Information processing method and apparatus for resource publishing

Abstract

The application discloses an information processing method and device for resource publishing, an electronic device, a non-transitory computer storage medium and a computer program product, and belongs to the field of data processing. The method comprises the following steps: in response to a publishing instruction for a resource to be published, determining a target resource template based on a resource template set, wherein the resource template set comprises a base template with common information elements, and at least one inherited template with extended information elements inherited from the base template; based on at least one unstructured document related to the resource to be published, first information is parsed from the unstructured document by using a first model; and based on the parsed first information, a structured field in the target resource template is filled in for the publishing of the resource to be published.

Description

Technical Field

[0001] This application belongs to the field of data processing, and particularly relates to an information processing method and apparatus for resource publishing, electronic equipment, non-transitory computer storage medium, and computer program product. Background Technology

[0002] In the information-based release and management of resources, business entities (such as enterprises) typically release and manage various resources (such as data services, API interfaces, equity products, applications, functional modules, etc.) through a resource release platform. In this process, resource templates are used as a tool for standardizing resource descriptions. However, resource templates are generally static and fixed in structure, unable to adapt to rapid changes in business needs. Their maintenance and updates heavily rely on manual intervention, resulting in insufficient agility.

[0003] Furthermore, the resource publishing process suffers from significant efficiency bottlenecks, heavily relying on business personnel to manually extract and input information from unstructured documents. This process is cumbersome, prone to errors, and makes it difficult to guarantee data quality. More importantly, the lack of an effective template optimization mechanism prevents templates from continuously improving in line with business practices. Therefore, a solution is needed that can automate and intelligently process resource publishing and automatically optimize templates. Summary of the Invention

[0004] This application provides an information processing method and apparatus for resource publishing, an electronic device, a non-transitory computer storage medium, and a computer program product.

[0005] In a first aspect, embodiments of this application provide an information processing method for resource publishing, comprising: responding to a publishing instruction for a resource to be published, determining a target resource template based on a resource template set, wherein the resource template set includes a base class template having common information elements, and at least one inherited class template having extended information elements inherited from the base class template; parsing first information from at least one unstructured document related to the resource to be published using a first model; and filling structured fields in the target resource template based on the parsed first information for publishing the resource to be published.

[0006] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the step of determining the target resource template further includes: selecting a inherited class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between at least one inherited class template and a base class template, as well as between the inherited class templates themselves, wherein all inherited class templates directly or indirectly inherit from the base class template; and determining the selected inherited class template and the inherited class templates and base templates that have inheritance relationships with it as the target resource template.

[0007] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the step of populating a structured field further includes: obtaining candidate population values ​​from multiple data sources for the same structured field, wherein the data sources include at least two of interface-type structured data sources, unstructured parsing data sources, and historical population data sources, and each type of data source is set with an initial confidence level; when the candidate population values ​​obtained from different data sources are inconsistent, determining a comprehensive score for each candidate population value based on a conflict reconciliation algorithm and the initial confidence level; and selecting the candidate population value with the highest comprehensive score as the population value of the structured field.

[0008] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the conflict reconciliation algorithm determines the comprehensive score based on the following formula: Comprehensive score = (initial confidence of the data source × weight α) + (timestamp confidence × weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1, and the timestamp confidence is determined based on the newness of the timestamp of the data corresponding to the candidate filling value.

[0009] According to one or more embodiments of the information processing method for resource publishing in this application, the step of selecting the fill value may optionally include: if the difference between the highest comprehensive score and the second highest comprehensive score is less than a preset difference threshold, then presenting inconsistent candidate fill values ​​and their data source information to the user; and determining the selection made by the user from the presented candidate fill values.

[0010] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the preset initial confidence levels of the interface-type structured data source, the unstructured parsed data source, and the historical data filling data source decrease sequentially.

[0011] The information processing method for resource publishing according to one or more embodiments of this application may optionally further include: using a second model to generate descriptive content related to the resource to be published based on unstructured documents and / or user input.

[0012] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the step of generating the description content further includes one or more of the following: flowchart generation: generating a corresponding business process flowchart based on the business process text description in user input or an unstructured document; image and text generation: generating corresponding image content based on user input or an unstructured document; video generation: generating corresponding video content based on user input or an unstructured document.

[0013] According to one or more embodiments of the information processing method for resource publishing according to this application, optionally, after the step of generating the description content, the method further includes: receiving prompt words input by a user for the generated description content; and modifying the generated description content using a second model based on the prompt words.

[0014] According to one or more embodiments of the present application, the information processing method for resource publishing may optionally further include: monitoring a first modification operation related to information elements performed by one or more users on a resource template when publishing resources; and determining whether to update the resource template based on the number of times the first modification operation is performed.

[0015] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the step of determining whether to update further includes: based on a reinforcement learning model, evaluating the optimization value of the first modification operation on the resource template according to the number of times the first modification operation is executed, wherein the first modification operation is preset to optimize the resource template; and in response to the cumulative evaluation result of the first modification operation reaching a predetermined evaluation threshold, updating the modification content corresponding to the first modification operation to the resource template.

[0016] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the first modification operation is preset to optimize the resource template, and further includes: assigning a positive reward value to the first modification operation, while assigning a zero value or a negative value to the operation that has not been modified.

[0017] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the reinforcement learning model is based on the Q-Learning reinforcement learning algorithm, defining a Q value for the state s of the resource template and the first modification operation a, wherein the state s represents the current state of the resource template, and wherein the following Q value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a' [Q(s',a')-Q(s,a)], where R represents the immediate reward value for the first modification operation a, s' represents the new state the resource template enters after executing the first modification operation a, and max... a' Q(s',a') represents the maximum Q value among all first modification operations in state s', α is the learning rate associated with the Q value update step size, and γ is the discount factor associated with immediate reward and future reward. The method further includes: when the Q value is updated to a predetermined evaluation threshold, updating the resource template according to the modification method of the first modification operation, and setting the Q value to 0.

[0018] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the first modification operation includes any one of the following: adding new information elements to the resource template, deleting existing information elements, or modifying existing information elements.

[0019] Optionally, the information processing method for resource publishing according to one or more embodiments of this application may further include: calling a data interface or executing a data query script according to a preset time period to obtain the latest dynamic data related to the resource, wherein the data interface information or data query script for obtaining the dynamic data of the resource is pre-configured; and using the latest dynamic data obtained to update the dynamic data of the published resource.

[0020] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the dynamic data updating step further includes: displaying the latest acquired data in the form of a visual chart.

[0021] According to one or more embodiments of the present application, the information processing method for resource publishing may optionally further include: triggering a structured field filling step based on receiving an updated unstructured document corresponding to the published resource, and updating the information of the published resource.

[0022] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the inherited class template is defined by inheriting all information elements of the base class template and adding extended information elements corresponding to the type of the resource to be published.

[0023] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, the step of parsing the first information using a first model further includes: performing optical character recognition, natural language understanding, and information extraction on the unstructured document.

[0024] According to one or more embodiments of the information processing method for resource publishing in this application, optionally, after parsing the first information, the first information is confirmed by the user before the filling operation based on the first information is performed.

[0025] Secondly, embodiments of this application provide an information processing apparatus for resource publishing, comprising: a template determination module configured to determine a target resource template based on a resource template set in response to a publishing instruction for a resource to be published, wherein the resource template set includes a base class template having common information elements and at least one inherited class template having extended information elements inherited from the base class template; an information parsing module configured to parse first information from at least one unstructured document related to the resource to be published using a first model; and a field filling module configured to fill structured fields in the target resource template based on the parsed first information for publishing the resource to be published.

[0026] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include a template determination module further configured to: select a inherited class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between at least one inherited class template and a base class template, as well as between the inherited class templates themselves, wherein each inherited class template directly or indirectly inherits from a base class template; and determine the selected inherited class template and the inherited class templates and base templates with which it has an inheritance relationship as the target resource template.

[0027] According to one or more embodiments of this application, the information processing apparatus for resource publishing may optionally include a field filling module comprising: a candidate value acquisition unit configured to: acquire candidate filling values ​​from multiple data sources for the same structured field, wherein the data sources include at least two of interface-type structured data sources, unstructured parsing data sources, and historical filling data sources, each type of data source having an initial confidence level; a conflict reconciliation unit configured to: determine a comprehensive score for each candidate filling value based on a conflict reconciliation algorithm and the initial confidence level when candidate filling values ​​acquired from different data sources are inconsistent; and a candidate value selection unit configured to: select the candidate filling value with the highest comprehensive score as the filling value for the structured field.

[0028] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include a conflict reconciliation unit further configured to determine a comprehensive score based on the following formula: comprehensive score = (initial confidence of the data source × weight α) + (timestamp confidence × weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1, and the timestamp confidence is determined based on the newness of the timestamp of the data corresponding to the candidate filling value.

[0029] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include a candidate value selection unit further configured to: present inconsistent candidate fill values ​​and their data source information to the user if the difference between the highest comprehensive score and the second highest comprehensive score is less than a preset difference threshold; and determine the selection made by the user from the presented candidate fill values.

[0030] According to one or more embodiments of the information processing apparatus for resource publishing in this application, optionally, the preset initial confidence levels of the interface-type structured data source, the unstructured parsed data source, and the historical data filling data source decrease sequentially.

[0031] An information processing apparatus for resource publishing according to one or more embodiments of this application may optionally further include: a content generation module configured to: generate descriptive content related to the resource to be published based on an unstructured document and / or user input using a second model.

[0032] According to one or more embodiments of the information processing apparatus for resource publishing in this application, optionally, the content generation module is further configured to perform one or more of the following: flowchart generation: generating a corresponding business process flowchart based on a business process text description input by the user or in an unstructured document; image and text generation: generating corresponding image content based on user input or an unstructured document; video generation: generating corresponding video content based on user input or an unstructured document.

[0033] An information processing apparatus for resource publishing according to one or more embodiments of this application may optionally further include: an interaction optimization module configured to: receive prompt words input by a user for generated description content; and modify the generated description content based on the prompt words using a second model.

[0034] An information processing apparatus for resource publishing according to one or more embodiments of this application may optionally further include: a template update module configured to: monitor a first modification operation related to information elements performed by one or more users on a resource template when publishing resources; and determine whether to update the resource template based on the number of times the first modification operation is performed.

[0035] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include a template update module further configured to: evaluate the optimization value of the first modification operation on the resource template based on a reinforcement learning model and the number of times the first modification operation is executed, wherein the first modification operation is preset to optimize the resource template; and update the modification content corresponding to the first modification operation to the resource template in response to the cumulative evaluation result of the first modification operation reaching a predetermined evaluation threshold.

[0036] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include a first modification operation preset to optimize a resource template, further comprising: assigning a positive reward value to the first modification operation, and assigning a zero or negative value to an operation that has not been modified.

[0037] According to one or more embodiments of the information processing apparatus for resource publishing in this application, optionally, the reinforcement learning model is based on the Q-Learning reinforcement learning algorithm, defining a Q value for the state s of the resource template and the first modification operation a, wherein the state s represents the current state of the resource template, and wherein the following Q value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a' [Q(s',a')-Q(s,a)], where R represents the immediate reward value for the first modification operation a, s' represents the new state the resource template enters after executing the first modification operation a, and max... a' Q(s',a') represents the maximum Q value among all first modification operations in state s', α is the learning rate related to the Q value update step size, and γ is the discount factor related to immediate reward and future reward. The template update module is further configured to update the resource template according to the modification method of the first modification operation when the Q value is updated to a predetermined evaluation threshold, and set the Q value to 0.

[0038] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include the first modification operation including any of the following: adding new information elements to the resource template, deleting existing information elements, or modifying existing information elements.

[0039] An information processing apparatus for resource publishing according to one or more embodiments of this application may optionally further include: a data update module configured to: call a data interface or execute a data query script according to a preset time period to obtain the latest dynamic data related to the resource, wherein the data interface information or data query script for obtaining the dynamic data of the resource is pre-configured; and update the dynamic data of the published resource using the latest dynamic data obtained.

[0040] According to one or more embodiments of the information processing apparatus for resource publishing in this application, optionally, the data update module is further configured to display the latest acquired data in the form of a visual chart.

[0041] An information processing apparatus for resource publishing according to one or more embodiments of this application may optionally further include: an information update module configured to: trigger a structured field filling step based on receiving an updated unstructured document corresponding to the published resource, and update the information of the published resource.

[0042] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally define an inherited class template by inheriting all information elements of a base class template and adding extended information elements corresponding to the type of the resource to be published.

[0043] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally include an information parsing module further configured to perform optical character recognition, natural language understanding, and information extraction on unstructured documents.

[0044] According to one or more embodiments of the present application, the information processing apparatus for resource publishing may optionally be further configured to: after the information parsing module parses the first information, the first information is confirmed by the user, and then the field filling module performs a filling operation based on the first information.

[0045] Thirdly, embodiments of this application provide an electronic device, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the information processing method for resource publishing according to various embodiments of the first aspect.

[0046] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the information processing method for resource publishing according to various embodiments of the first aspect.

[0047] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the information processing method for resource publishing according to various embodiments of the first aspect. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] Figure 1 This is a flowchart of an information processing method for resource publishing provided according to some embodiments of this application.

[0050] Figure 2 This is a schematic diagram illustrating the relationship between base class templates and inherited class templates provided according to some embodiments of this application.

[0051] Figure 3 This is an example schematic diagram of a resource publishing process according to one embodiment of this application.

[0052] Figure 4 This is a schematic diagram of the structure of an information processing apparatus for resource publishing provided according to some embodiments of this application.

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device provided according to some embodiments of this application. Detailed Implementation

[0054] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples. It should be noted that the acquisition, storage, use, and processing of information and data in the embodiments of this application are all authorized by users or relevant organizations and comply with the relevant provisions of national laws and regulations.

[0055] In this article, the term "resource" refers to any entity or service that needs to be described, managed, shared, and used digitally, encompassing various functional units or assets that can be encapsulated and provided. For example, resources may include, but are not limited to: data services (such as data query interfaces and data analysis models), application programming interfaces (APIs), software applications, software functional modules, computing resources (such as cloud servers and storage space), digital content (such as audio and video and documents), benefit products (such as membership privileges and coupons), and more broadly, business capabilities or solutions. Resources can be used internally by an entity (such as a company) or can be made available to external partners or end users. Resources can be abstracted into a series of standardized attributes (information elements) for description and can be published and scheduled through a platform.

[0056] In this article, the terms "resource publishing," "resource release," or simply "publishing" refer to a series of operations and processes that standardize the descriptive information of a "resource" and input it into a centralized platform or system, making it discoverable, understandable, invoked, combined, or managed. This process realizes the "going online" or "being listed" of a resource, transforming it from an isolated state into a shareable and usable one. "Resource publishing" includes not only the initial creation and registration but may also encompass subsequent updates, version iterations, and delisting. This includes filling in or generating resource metadata (such as name, description, and version), defining access interfaces or usage methods, configuring policies (such as security policies and billing policies), setting lifecycle management rules, and associating them with relevant unstructured documents (such as manuals and technical specifications) to promote resource reuse, simplify resource integration, and support full lifecycle management of resources.

[0057] The term "resource template" or "template" refers to a predefined structured framework or pattern used to standardize the description of "resources." A "resource template" defines a set of information elements (also known as fields, attributes, or metadata) required to describe a specific type of resource, the data type of each element, format constraints, and the logical relationships between them. "Resource templates" ensure consistency in the description of similar resources, thereby achieving standardization and automated processing of resources. For example, as described later in the embodiments of this disclosure, a "base class template" can define common attributes (such as ID, name, creator) for all resources, while different "inherited class templates" (such as "API template" or "data service template") can inherit from the base class template and add their unique extended attributes (such as "API endpoint" or "data format"). The form of a "resource template" can be an XML Schema, JSON Schema, database table structure, or a form structure configured in a graphical interface.

[0058] The terms "user," "resource publisher," or "resource organizer" refer to the entity that performs the "resource publishing" operation, i.e., the entity that interacts with the resource publishing platform. These include natural persons (such as employees, developers, product managers, or operations personnel from different business units within a company); legal entities or organizational entities (such as a business unit or a partner company); and other software systems or automated agents (such as an automated program in a continuous integration / continuous deployment pipeline) that are authorized to perform resource publishing-related operations. These entities may be responsible for providing, reviewing, confirming, and managing resource information.

[0059] The term "model" here refers to a shorthand for artificial intelligence-related models or machine learning-related models, specifically computational models trained on data using algorithms that can perform specific cognitive tasks. In the context of this article, a model is one of the automated components of the resource publishing process. The scope of "models" includes, but is not limited to: large language models, used to understand and generate natural language, performing tasks such as extracting information from unstructured text, content summarization, and descriptive polishing; multimodal models, capable of processing and understanding various types of data (such as text, images, and audio), used to generate text-to-images, text-to-video, and intelligent flowcharts; computer vision models (such as those used for OCR optical character recognition); and reinforcement learning models, used for decision-making through interaction with the environment. These models can be pre-trained general-purpose models or specialized models fine-tuned for specific domains or tasks, and their deployment can be in the form of cloud API services or local / lightweight model files.

[0060] Given that various business systems (such as enterprise online platforms or applications) gradually build multiple resource management and service platforms during long-term operation, in some cases, due to differences in construction period, technical routes, and goals, each platform is usually designed around specific resource attributes, resulting in scattered storage and heterogeneous processing of resource data, leading to varying degrees of data isolation. This situation makes it difficult to communicate and manage resource information across platforms, limiting the effective integration and collaborative utilization of resources. Furthermore, in traditional standardized resource publishing practices, resource registration and maintenance heavily rely on manual operations. Administrators need to manually enter various resource information into preset templates, and dynamic information such as operational statistics and status updates also requires regular manual maintenance, making the entire process cumbersome and inefficient.

[0061] The following describes the information processing method, electronic device, non-transitory computer storage medium, and computer program product for resource publishing provided in this application.

[0062] This application provides an information processing method for resource publishing, applicable to scenarios requiring the publishing of various resources, such as the publishing and updating of new business functions, applications, and data and information. This information processing method for resource publishing can be executed by electronic devices (e.g., computer equipment, physical servers, cloud server hardware systems, etc.). Figure 1 A flowchart of an information processing method for resource publishing provided in an embodiment of this application is shown below. Figure 1 As shown, the information processing method for resource publishing may include steps S101 to S103.

[0063] In step S101, in response to a release instruction for a resource to be released, a target resource template is determined based on a resource template set. This resource template set includes a base class template with common information elements, and at least one inherited class template with extended information elements. The base class template records common information elements that all types of resources generally need to possess, also known as basic information elements, such as resource ID, resource name, creator, creation date, status, and basic description. This ensures consistency and convenience in the release of various resources at the basic level of resource template usage. Inherited class templates (e.g.) Figure 2 The information-type templates and rights-type templates shown are derived from the base class templates through an inheritance mechanism. For example, they can contain all or most of the common information elements of the base class templates. Based on this, they extend with additional information elements specific to the application scenario or function, according to the business characteristics of specific resource types (such as payment, content, marketing, etc.). For instance, the "Procurement Rights Template," while inheriting all elements from the "Rights Template" and the "Base Class Template," will extend with fields such as "Supplier Information," "Procurement Contract Number," and "Settlement Cycle." This hierarchical design makes the resource template set both standardized, flexible, and scalable, enabling rapid adaptation to new resource types and avoiding the problems of difficulty in modification and flexible adaptation associated with fixed static resource templates. For example, inherited templates can be defined by inheriting all information elements from the base class template and adding extended information elements corresponding to the type of resource to be published. Specifically, see attached... Figure 2 The resource template tree diagram shown illustrates a hierarchical inheritance relationship. For example, the "Procurement Rights Template" corresponding to the specific resource "Airport VIP Lounge" indirectly inherits from the top-level "Resource Base Class Template." In a practical example, when a user needs to publish the "Airport VIP Lounge" resource, the system automatically associates or allows the user to select the most matching underlying inherited class template (i.e., the "Procurement Rights Template") as the target resource template to use (populate). At this point, the target resource template actually contains all the information elements defined in its parent templates (including "Rights Templates," "Funds Templates," and even the top-level "Base Class Template"), as well as its own defined extended elements. This ensures the completeness and consistency of descriptions during resource publishing and management, while making resource template management and maintenance more efficient. When publishing a resource, there's no need to define it from scratch, improving the flexibility and speed of resource template usage.

[0064] In step S102, based on at least one unstructured document related to the resource to be published, a first model is used to parse the first information from the unstructured document. For example, a large language model can be used to free users from tedious manual data entry. Users typically already possess unstructured documents related to their resource to be published, such as product manuals, technical white papers, user manuals, and business contracts (these can be in formats such as Word, PDF, PPT, Excel, images, audio, and video). Manually reading, understanding, and extracting key information to fill in forms is inefficient and error-prone. In embodiments of this disclosure, users only need to upload all these related documents once during the publishing process. The system then calls the first model (e.g., a pre-trained large language model LLM) to automatically parse the uploaded documents. For example, the step of parsing the first information using the first model can further include: performing optical character recognition, natural language understanding, and information extraction on the uploaded unstructured document. The process first uses optical character recognition (OCR) technology to convert non-text content such as images and scanned documents into processable text information. Then, it uses natural language understanding technology to perform deep semantic analysis on the text. Finally, it uses information extraction technology to accurately identify and extract key information corresponding to the structured fields in the resource template, such as resource name, function description, technical parameters, and contact information. This extracted information is the first information.

[0065] For example, when a user uploads a product introduction PDF containing images, the system first uses an OCR component to recognize the text in the images, ensuring that all content can be read by the machine. Next, the large language model performs natural language understanding on the integrated full text, recognizing the document's chapter structure, semantic paragraphs, and key entities. Finally, the model performs targeted information extraction based on predefined field rules in the resource template. For instance, the model understands that a sentence like "This product aims to provide..." generally describes a product introduction and extracts that text as a candidate value for the "Resource Introduction" field; similarly, it can accurately extract "Zhang San" as the value for the "Contact Person" field from "Technical Support Contact Person: Zhang San". This improves the accuracy, speed, and breadth of information extraction.

[0066] In step S103, the structured fields in the target resource template are populated based on the parsed first information. After parsing the first information from the unstructured document, this information can be automatically populated into the corresponding structured fields of the target resource template determined in step S101. For example, the "supplier name" extracted from the contract PDF will be populated into the "supplier" field of the template, and the core function description extracted from the product introduction will be populated into the "service highlights" field. This step transforms the unstructured, messy document content into standardized, structured data.

[0067] Furthermore, step S103 can optionally involve having the user confirm the first information after it has been parsed in step 102 before performing the data entry operation based on that first information. In other words, a human-computer collaborative interaction mode can also be adopted. For example, after parsing and suggesting data entry, the data is not immediately saved; instead, the parsed first information and / or the results of the data entry are displayed to the resource publisher (user) for confirmation. The user interface can indicate which information was automatically extracted by the model, allowing the user to easily modify, adjust, or supplement it. In this alternative, the user has the right to confirm; the data entry only becomes effective after the user verifies that it is correct and clicks "confirm." This preserves the user's review and confirmation role, ensuring the quality of the resource information.

[0068] After the population operation, it can be used for the release of resources. For example, all the populated standardized resource information and fields can be saved to complete the resource release process. At this point, the resource can be retrieved, discovered, and authorized for use by other business departments or partners. Because the resource information is structured, in some examples it can be easily called by other systems through API interfaces, or flexibly orchestrated and combined to quickly form new product solutions.

[0069] In some embodiments, reference Figure 2 Based on the schematic diagram of the relationship structure between base class templates and inherited class templates provided in some embodiments of this application, the step of determining the target resource template in some examples of the information processing method for resource publishing disclosed herein further includes: selecting an inherited class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between at least one inherited class template and the base class template, as well as between the inherited class templates themselves, wherein all inherited class templates directly or indirectly inherit from the base class template; and determining the selected inherited class template and the inherited templates and base templates that have inheritance relationships with it as the target resource template.

[0070] like Figure 2As shown, the entire resource template set is presented in a tree-like hierarchical structure. This structure demonstrates the inheritance relationships between resource templates. For example, the "Airport VIP Lounge" feature to be published (represented as Service A in the diagram) can use the "Procurement-type Benefit Template," which inherits from the "Benefit-type Template." The "Benefit-type Template" inherits from the "Funds-type Template," and the "Funds-type Template" ultimately inherits from the top-level "Resource Base Class Template." This tree structure can be displayed when a user needs to publish a resource. In addition, the Business Cooperation-type Benefit Template can implement a B benefit, and the Project-type Benefit Template can implement a C product. Service A, B benefit, and C product are all example resource publishing implementations and can be used interchangeably. Users can select along the branches of the tree based on the type of resource to be published, for example, locating and selecting the "Procurement-type Benefit Template." When determining the target resource template, this resource template and all its inherited resource templates (i.e., all parent templates upwards along the inheritance chain) can be automatically included. Therefore, the final target resource template is actually a collection of templates that contains all information elements defined along the entire inheritance chain from the base class template to the finally selected inherited class template.

[0071] In some embodiments, the structured field population step of some examples of the information processing method for resource publishing disclosed herein further includes: obtaining candidate population values ​​from multiple data sources for the same structured field, wherein the data sources include at least two of interface-type structured data sources, unstructured parsing data sources, and historical population data sources, each type of data source having an initial confidence level; when candidate population values ​​obtained from different data sources are inconsistent, determining a comprehensive score for each candidate population value based on a conflict reconciliation algorithm and the initial confidence level; and selecting the candidate population value with the highest comprehensive score as the population value for the structured field.

[0072] Specifically, for the same field to be populated (such as "service provider"), candidate values ​​can be retrieved from multiple data sources in parallel. In some examples, the initial confidence levels of the interface-based structured data source, unstructured parsed data source, and historical data entry data source can be preset to decrease sequentially. These data sources include: 1) Interface-based structured data sources: such as a unified product database within an enterprise or an external official authoritative database (such as information from the Ministry of Industry and Information Technology's filing system). Their data standards are high and authoritative, so the initial confidence level can be preset to the highest (e.g., 0.9); 2) Unstructured parsed data sources: information parsed from user-uploaded documents using the first model. While this includes some details, it may contain parsing errors, so the confidence level can be preset to medium (e.g., 0.5); 3) Historical data entry data sources: records from one or more users (resource publishers or administrators) or similar business parties. These may contain outdated or erroneous information, so the confidence level can be preset to the lowest (e.g., 0.2). When candidate values ​​from different data sources are inconsistent, a conflict reconciliation algorithm can be activated. This algorithm considers not only the initial confidence level of the data sources but also the timestamps (timeliness) of the data, calculating a comprehensive score through weighted averages. In this way, the most reliable and timely data can be automatically selected as the fill value, improving the accuracy and quality of the filled data.

[0073] For example, the conflict reconciliation algorithm can determine the overall score based on the following formula: Overall Score = (Initial Confidence of Data Source × Weight α) + (Timestamp Confidence × Weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1. The timestamp confidence is determined based on the freshness of the timestamps of the data corresponding to the candidate fill values. For example, for basic information that changes slowly (such as enterprise qualifications), α can be set to be larger (e.g., 0.7) and β smaller (e.g., 0.3), thus focusing more on the authority of the data source. For dynamic information that changes frequently (such as the number of monthly active users), β can be set to be larger, focusing more on timeliness. The timestamp confidence is a value calculated based on the freshness of the data; generally, the newer the data, the higher its timestamp confidence. Through this dynamically weighted algorithm, more accurate fill value selection can be made in the case of multi-source data conflicts. In some embodiments, the fill value selection step of some examples of the information processing method for resource publishing disclosed herein further includes: if the difference between the highest and second-highest comprehensive scores is less than a preset difference threshold, presenting inconsistent candidate fill values ​​and their data source information to the user; and determining the user's choice from the presented candidate fill values. That is, in some examples, a human-machine collaborative decision-making mechanism can be employed. When the comprehensive scores of two candidate values ​​are very close (the difference is less than a preset threshold), it indicates that the credibility of the two data sources is similar. At this time, for example, a list to be confirmed can be generated, showing the user each conflicting candidate value, their data source, confidence level, and timestamp, allowing the user to make a final selection.

[0074] In some embodiments, examples of the information processing method for resource publishing disclosed herein may further include: using a second model to generate descriptive content related to the resource to be published, based on unstructured documents and / or user input. Besides filling structured fields, rich unstructured content (such as images, videos, and flowcharts) is beneficial for resource display and promotion. This content can be automatically generated using a second model (e.g., a multimodal AI model, such as text-to-image or text-to-video models). For example, high-quality resource promotional images, introductory videos, business process diagrams, etc., can be automatically generated based on parsed unstructured document content or directly from a brief description (prompt) input by the user. This lowers the barrier and cost of content creation during the resource publishing process.

[0075] Specifically, the steps for generating the description content can be further included, for example, one or more of the following: flowchart generation: generating a corresponding business process diagram based on the textual description of the business process from user input or unstructured documents; image and text generation: generating corresponding image content based on user input or unstructured documents; video generation: generating corresponding video content based on user input or unstructured documents. For example, if a user provides a textual description such as "User Login -> Select Service -> Place Order and Pay -> Verification Completed," the second model can automatically convert it into a standard UML activity diagram or flowchart; the second model can summarize the description of the resources to be released and generate, for example, promotional posters or case cover images, short promotional videos, etc.

[0076] Furthermore, for example, after the step of generating the description content, the method further includes: receiving prompts input by the user regarding the generated description content; and modifying the generated description content using a second model based on the prompts. That is, in some examples, the generated description content is not finalized at once. Interactive optimization functionality can be provided. For example, if a user is dissatisfied with the result generated by the second model (such as an image or video), they can provide suggestions for modification by inputting natural language prompts (e.g., "The image style should be more business-like," "The video pace should be faster"). The second model will understand the user's prompts and iteratively optimize the generated content accordingly until the user is satisfied.

[0077] In some embodiments, examples of the information processing method for resource publishing disclosed herein may further include: monitoring one or more users performing a first modification operation related to information elements on a resource template when publishing resources; and determining whether to update the resource template based on the number of times the first modification operation is performed. For example, the modification operations (i.e., the first modification operations) made by user groups (e.g., various resource publishers) on the resource template itself when publishing resources using the resource template can be continuously monitored. For example, a user finds that a resource template is missing a field and manually adds a note. The number or frequency of each specific modification operation (e.g., "add 'holiday schedule' field") performed by different users can be counted. When a specific modification operation is widely adopted (i.e., the number of times reaches a certain threshold), it is determined that it represents a common optimization need, and this modification can be automatically updated into the template. This allows the template to continuously learn and evolve from subsequent use by the user group, solving the problem of rigidity in traditional templates.

[0078] The step of determining whether to update the resource template further includes: based on a reinforcement learning model, evaluating the optimization value of the first modification operation on the resource template according to the number of times the first modification operation is executed, wherein the first modification operation is preset to optimize the resource template; and updating the modified content corresponding to the first modification operation to the resource template in response to the accumulated evaluation results of the first modification operation reaching a predetermined evaluation threshold. Thus, each modification operation by the user is considered an "action," and the reinforcement learning model evaluates the long-term value (optimization value) of that action. The reinforcement learning model can be set to a basic policy that defaults to the user's modification behavior optimizing the template. Therefore, whenever a specific modification action is executed, the model will give a positive reward and accumulate the optimization value of that action (e.g., quantified using a Q-value). When this optimization value accumulates to exceed a preset evaluation threshold, it indicates that the optimization has been sufficiently validated, and the action can be automatically executed to formally update the template.

[0079] Furthermore, the first modification operation described above, presupposed to optimize the resource template, can be further enhanced by assigning a positive reward value to the first modification operation and a zero or negative value to operations that do not involve modification. In a reinforcement learning model, this can be implemented, for example, through a reward function. Specifically, when it is detected that a user has performed an operation to modify the template (the first modification operation), a positive reward value (such as +1) is assigned. Conversely, if the user uses the template directly without making any modifications, a zero or negative reward (such as -1 or 0) is assigned. This reward mechanism guides the reinforcement learning model to learn modification behaviors that optimize the resource template, thereby driving continuous improvement of the template.

[0080] For example, a reinforcement learning model based on the Q-Learning reinforcement learning algorithm defines a Q-value for a resource template with state s and a first modification operation a, where state s represents the current state of the resource template, and the following Q-value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a' Q(s',a')-Q(s,a)], Where R represents the immediate reward value for the first modification operation a, s' represents the new state the resource template enters after executing the first modification operation a, and max a' Q(s',a') represents the maximum Q-value among all first modification operations in state s', α is the learning rate associated with the Q-value update step size, and γ is the discount factor associated with immediate and future rewards. Therefore, some examples of the information processing method for resource publishing disclosed herein may further include: when the Q(s,a) value is updated to a predetermined evaluation threshold, updating the resource template according to the modification method of the first modification operation, and setting the Q-value to 0.

[0081] Specifically, in some application scenarios disclosed herein, the "state s of the resource template" can be concretized as the overall state or overall value of the set of information elements currently contained in the template. The initial state s1 represents the initial overall value of the template. The "first modification operation a" can, for example, be defined as an atomic modification action on a specific information element. For example, a specific action a... x This can be defined as "adding a field named 'Service Level' with a data type of 'Text' to the 'Service Resource Template'", rather than a generic "add field" operation. This ensures that each monitored and learned modification operation has clear semantics and an operand, avoiding confusion between different modification intentions. When a user performs such a modification to the template during resource publishing, this modification event can be automatically recorded.

[0082] The reward value R is used to guide the learning direction of this model. For example, an aggressive reward strategy can be adopted, which assumes that the user's modification behavior is an optimization of the template. Therefore, once a specific action a is detected... x In other words, a positive immediate reward R is given (e.g., R = +1). Conversely, if the user does not make any changes to the template, a zero or negative reward is given (e.g., R = 0 or -1). This reward mechanism essentially guides the reinforcement learning model to pay attention to the user's modifications and predicts the direction of template improvement through cumulative effects.

[0083] The above formula assesses the long-term value of a specific modification. R represents the immediate reward gained after performing the modification, reflecting its instantaneous value. The formula also evaluates the present value (weighted by a discount factor γ) of the maximum possible cumulative reward after the resource template enters a new state s' following the modification. The learning rate α controls the impact of this update on the original Q-value. Through repeated iterations (i.e., each time a user performs or does not perform the modification), the Q-value of this specific modification in a specific template state gradually converges, eventually stably reflecting the prevalence of its adoption by the user group. For example, a modification to a resource template with improvement value (such as adding a specific field) will be repeatedly performed by more and more users when publishing similar resources, causing its Q-value to continuously increase due to the cumulative positive rewards.

[0084] When the accumulated Q-value corresponding to a certain modification action exceeds a preset evaluation threshold (e.g., the threshold is set to 10), it indicates that the modification action has been performed a sufficient number of times, representing a consensus among one or more users on this modification operation. At this point, the template update process can be automatically triggered: the modified content defined by the specific modification action (operation) (such as adding a specific field) is formally solidified into the definition of the resource template, thereby generating a new version of the template. Subsequently, the Q-value corresponding to the specific action can be reset to 0, thus allowing monitoring of user modification behavior for this new template version (new state s) to restart, thereby initiating the next round of learning and optimization cycles, achieving continuous automatic optimization of the resource template.

[0085] In addition, the first modification operation may include, for example, any of the following: adding a new information element to the resource template (e.g., adding a structured field to the resource template), deleting an existing information element (e.g., deleting a structured field from the resource template), or modifying an existing information element (e.g., changing the data type of a field from "text" to "enumeration", or modifying its constraints).

[0086] In some embodiments, examples of the information processing method for resource publishing disclosed herein further include: calling a data interface or executing a data query script according to a preset time period to obtain the latest dynamic data related to the resource, wherein the data interface information or data query script used to obtain the dynamic data of the resource is pre-configured; and using the obtained latest dynamic data to update the dynamic data of the published resource. That is, the user can pre-configure the data source (such as an API interface address) or a data query script when or after resource publishing. Therefore, according to a preset time period, the interface or script can be automatically called to pull the latest data from the backend data platform. Then, this latest data can be used to automatically update the dynamic information of the published resource displayed, for example, on the front end, thereby ensuring that the dynamic data statistics of the resource are always real-time and accurate, without the need for manual repetitive updates.

[0087] Furthermore, the dynamic data update process can, for example, include displaying the latest acquired data in the form of a visual chart. In the example, in addition to automatically updating the dynamic data, the dynamic data in numerical form (such as monthly access trends) can be further visualized using chart components (such as line charts and bar charts) and displayed on the resource details page, enhancing the user's visual experience of the resources.

[0088] In some embodiments, examples of the information processing method for resource publishing disclosed herein further include: triggering a structured field population step based on receiving an updated unstructured document corresponding to the published resource, thereby updating the information of the published resource. When the original document related to the resource changes (such as a new version of the technical specification released for a product upgrade), the user only needs to re-upload the updated document. Upon receiving the new document, the aforementioned parsing and population process (i.e., steps S102-S103) can be automatically triggered to parse the information in the new document and compare it with the existing information of the resource to generate an update suggestion. After user confirmation, the online information update of the resource can be completed, thereby achieving timely and convenient updates of resource information and document versions.

[0089] To facilitate understanding, the following is an example of a human-computer collaboration process for information processing methods used in resource publishing. Figure 3 This is an example diagram illustrating a resource publishing process according to one embodiment of this application, such as... Figure 3As shown, the example resource publishing process begins with manual user action. In step 1, the user clicks "Publish Resource" to trigger the entire publishing process. The user then performs step 2.1, uploading relevant resource documents (providing unstructured documents and materials such as product descriptions and technical specifications). Next, the AI ​​model performs step 2.2, using a large language model to match structured data from the uploaded documents within the resource template (by parsing unstructured text and extracting information such as names and descriptions for fields to be filled in). Following this, the user performs step 2.3 to confirm and adjust the results generated by the large language model, reviewing and fine-tuning the extracted information to ensure accuracy. The process then moves to step 3.1, where a multimodal model matches unstructured data from the uploaded documents to generate images, promotional videos, etc. For example, the multimodal model automatically creates visual promotional materials based on document content. The user then performs step 3.2, confirming the unstructured data generated by the large model and receiving adjustment prompts. The user reviews the generated descriptions of images, videos, etc., and can input natural language commands (prompts) to request optimization. Next, in step 4.1, users need to create a case study for the resource, providing textual materials such as case introductions and business processes to provide specific application scenarios and operational procedures. The model then utilizes these case introductions and previously uploaded documents in step 4.2, leveraging intelligent flowchart capabilities to intelligently generate flowcharts from the business process text, or transform text descriptions into intuitive, standardized charts. In step 4.3, it uses text-to-graphics capabilities to summarize case highlights and intelligently generate a case cover, such as automatically extracting highlights and generating summary images. Finally, in step 5.1, users upload data reports and scripts on resource usage to provide raw data and automatic update logic. In step 5.2, the platform automatically parses the reports to generate visual data reports and updates the data periodically according to the script, achieving automatic generation of data charts and subsequent unattended scheduled updates. This completes the entire process. This embodiment achieves rapid, automatic, and information-rich resource publishing, improving the efficiency and quality of the resource publishing process.

[0090] Figure 4 This is a schematic diagram of the structure of an information processing apparatus for resource publishing provided according to some embodiments of this application. It should be noted that this information processing apparatus 400 corresponds to the aforementioned information processing method for resource publishing. All implementations in the method embodiments described above are applicable to the embodiments of this apparatus and can achieve the same technical effect. Furthermore, the details of each embodiment described above regarding the method embodiments are applicable or applicable according to the apparatus, and will not be repeated here.

[0091] The information processing device 300 for resource publishing may include a template determination module 301, an information parsing module 302, and a field filling module 303.

[0092] The template determination module 301 can be configured to: in response to a release instruction for a resource to be released, determine a target resource template based on a resource template set, wherein the resource template set includes a base class template with common information elements, and at least one inherited class template with extended information elements that inherits from the base class template; Information parsing module 302 can be configured to: based on at least one unstructured document related to the resource to be published, parse first information from the unstructured document using a first model; and The field filling module 303 can be configured to fill the structured fields in the target resource template based on the parsed first information for the publication of the resource to be published.

[0093] In some embodiments, the template determination module 301 may be further configured to: select a inherited class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between at least one inherited class template and a base class template, as well as between the inherited class templates themselves, wherein all inherited class templates directly or indirectly inherit from the base class template; and determine the selected inherited class template and the inherited class templates and base templates with which it has an inheritance relationship as the target resource template.

[0094] In some embodiments, the field filling module 303 may further include a candidate value acquisition unit, a conflict reconciliation unit, and a candidate value selection unit. The candidate value acquisition unit may be configured to acquire candidate values ​​from multiple data sources for the same structured field, wherein the data sources include at least two of the following: interface-type structured data sources, unstructured parsing data sources, and historical filling data sources, each type of data source having an initial confidence level. The conflict reconciliation unit may be configured to determine a comprehensive score for each candidate value based on a conflict reconciliation algorithm and the initial confidence level when candidate values ​​acquired from different data sources are inconsistent. The candidate value selection unit may be configured to select the candidate value with the highest comprehensive score as the fill value for the structured field.

[0095] In some embodiments, the conflict reconciliation unit is further configured to determine the overall score based on the following formula: Overall score = (Initial confidence of the data source × weight α) + (Timestamp confidence × weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1, and the timestamp confidence is determined based on the newness of the timestamp of the data corresponding to the candidate fill value.

[0096] In some embodiments, the candidate value selection unit is further configured to: present inconsistent candidate fill values ​​and their data source information to the user if the difference between the highest comprehensive score and the second highest comprehensive score is less than a preset difference threshold; and determine the selection made by the user from the presented candidate fill values.

[0097] In some embodiments, the preset initial confidence levels of the interface-type structured data source, unstructured parsed data source, and historical data filling data source involved in the information processing apparatus of this disclosure decrease sequentially.

[0098] In some embodiments, the information processing apparatus may further include a content generation module configured to generate descriptive content related to the resource to be published, based on unstructured documents and / or user input, using a second model.

[0099] In some embodiments, the content generation module may further be configured to perform one or more of the following: flowchart generation, generating a corresponding business process diagram based on a business process text description in a user-inputted or unstructured document; image and text generation, generating corresponding image content based on a user-inputted or unstructured document; and video generation, generating corresponding video content based on a user-inputted or unstructured document.

[0100] In some embodiments, the information processing apparatus may further include an interaction optimization module configured to receive prompts input by a user for the generated description content; and configured to modify the generated description content based on the prompts using a second model.

[0101] In some embodiments, the information processing apparatus further includes a template update module configured to monitor a first modification operation related to information elements performed on a resource template by one or more users when publishing resources; and to determine whether to update the resource template based on the number of times the first modification operation is performed.

[0102] In some embodiments, the template update module of the information processing device may be further configured to: evaluate the optimization value of the first modification operation on the resource template based on the number of times the first modification operation is executed, according to a reinforcement learning model, wherein the first modification operation is preset to optimize the resource template; and update the modification content corresponding to the first modification operation to the resource template in response to the cumulative evaluation result of the first modification operation reaching a predetermined evaluation threshold.

[0103] In some embodiments, the first modification operation involved in the information processing device is preset to optimize the resource template, and further includes: assigning a positive reward value to the first modification operation, while assigning a zero value or a negative value to the operation that is not modified.

[0104] In some embodiments, the reinforcement learning model involved in the information processing apparatus is based on the Q-Learning reinforcement learning algorithm, defining a Q-value for the state s of the resource template and the first modification operation a, wherein the state s represents the current state of the resource template, and wherein the following Q-value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a'Q(s',a')-Q(s,a)], Where R represents the immediate reward value for the first modification operation a, s' represents the new state the resource template enters after executing the first modification operation a, and max a' Q(s',a') represents the maximum Q-value among all first-modification operations in state s', α is the learning rate associated with the Q-value update step size, and γ is the discount factor associated with immediate and future rewards. Thus, the template update module of the information processing device can be further configured to: when the Q value is updated to a predetermined evaluation threshold, update the resource template according to the modification method of the first modification operation, and set the Q value to 0.

[0105] In some embodiments, the first modification operation involved by the information processing apparatus includes any one of the following: adding a new information element to a resource template, deleting an existing information element, or modifying an existing information element.

[0106] In some embodiments, the information processing apparatus may further include a data update module configured to: call a data interface or execute a data query script according to a preset time period to obtain the latest dynamic data related to the resource, wherein the data interface information or data query script used to obtain the dynamic data of the resource is pre-configured; and update the dynamic data of the published resource using the latest dynamic data obtained.

[0107] In some embodiments, the data update module of the information processing device may be further configured to display the latest acquired data in the form of a visual chart.

[0108] In some embodiments, the information processing apparatus may further include an information update module configured to: trigger a structured field population step based on receiving an updated unstructured document corresponding to the published resource, and update the information of the published resource.

[0109] In some embodiments, the inherited class template involved in the information processing apparatus is defined by inheriting all information elements of the base class template and adding extended information elements corresponding to the type of the resource to be published.

[0110] In some embodiments, the information parsing module of the information processing device may be further configured to perform optical character recognition, natural language understanding, and information extraction on unstructured documents.

[0111] In some embodiments, the information processing device may be further configured to: after the information parsing module parses the first information, the first information is confirmed by the user, and then the field filling module performs a filling operation based on the first information.

[0112] This application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, as shown below. Figure 5 As shown, the electronic device 500 includes a memory 501, a processor 502, and a computer program stored in the memory 501 and executable on the processor 502.

[0113] In some examples, the processor 502 described above may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or a graphics processing unit (GPU), a system-on-a-chip (SOC), or may be configured to implement one or more integrated circuits of the embodiments of this application.

[0114] Memory 501 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the information processing method for resource publishing according to embodiments of this application.

[0115] The processor 502 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 501, so as to implement the information processing method for resource publishing in the above embodiments.

[0116] In some examples, the electronic device 500 may also include a communication interface 503 and a bus 504. For example, Figure 5 As shown, the memory 501, processor 502, and communication interface 503 are connected through bus 504 and complete communication with each other.

[0117] The communication interface 503 is mainly used to enable communication between various modules, devices, units, and / or equipment in the embodiments of this application. Input devices and / or output devices can also be connected through the communication interface 503.

[0118] Bus 504 includes hardware, software, or both, that couples the components of electronic device 500 together. For example, and not as a limitation, bus 504 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-E) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 504 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0119] This application also provides a computer-readable storage medium storing computer program instructions. When these computer program instructions are executed by a processor, they can implement the information processing method for resource publishing described in the above embodiments and achieve the same technical effect. To avoid repetition, further details are omitted here. The aforementioned computer-readable storage medium may include non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc., and is not limited thereto.

[0120] This application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the information processing method for resource publishing in the above embodiments and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0121] It should be clarified that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. For the device embodiments, equipment embodiments, computer-readable storage medium embodiments, and computer program product embodiments, the relevant parts can be referred to the description section of the method embodiments. This application is not limited to the specific steps and structures described above and shown in the figures. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application. Furthermore, for the sake of brevity, detailed descriptions of known methods and techniques are omitted here.

[0122] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0123] Those skilled in the art will understand that the above embodiments are exemplary and not restrictive. Different technical features appearing in different embodiments can be combined to achieve beneficial effects. Based on a study of the drawings, specification, and claims, those skilled in the art should be able to understand and implement other variations of the disclosed embodiments. In the claims, the term "comprising" does not exclude other means or steps; the quantifier "a" does not exclude a plurality; the terms "first" and "second" are used to identify names and not to indicate any particular order. No reference numerals in the claims should be construed as limiting the scope of protection. The functionality of multiple parts appearing in the claims can be implemented by a single hardware or software module. The appearance of certain technical features in different dependent claims does not mean that these technical features cannot be combined to achieve beneficial effects.

Claims

1. An information processing method for resource publishing, characterized in that, The method includes: In response to a release instruction for a resource to be released, a target resource template is determined based on a set of resource templates, wherein the set of resource templates includes a base class template with common information elements, and at least one inherited class template with extended information elements that inherits from the base class template. Based on at least one unstructured document associated with the resource to be published, first information is parsed from the unstructured document using a first model; and Based on the parsed first information, the structured fields in the target resource template are populated for use in publishing the resource to be published.

2. The method according to claim 1, characterized in that, The step of determining the target resource template further includes: Select a class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between the at least one class template and the base class template, as well as between the class templates themselves, wherein each class template inherits directly or indirectly from the base class template. The selected inheritance class template, the inheritance class templates that have inheritance relationships with it, and the base class template are determined as the target resource template.

3. The method according to claim 1, characterized in that, The step of populating the structured fields further includes: For the same structured field, candidate fill values ​​are obtained from multiple data sources, wherein the data sources include at least two of the following: interface-type structured data sources, unstructured parsed data sources, and historical fill data sources, and each type of data source is set with an initial confidence level. When candidate imputation values ​​obtained from different data sources are inconsistent, a comprehensive score is determined for each candidate imputation value based on the conflict reconciliation algorithm and the initial confidence level; and The candidate fill value with the highest overall score is selected as the fill value for the structured field.

4. The method according to claim 3, characterized in that, The conflict reconciliation algorithm determines the overall score based on the following formula: The overall score is calculated as follows: (Initial confidence of the data source × weight α) + (Timestamp confidence × weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1. The timestamp confidence is determined based on the newness of the timestamps of the data corresponding to the candidate fill values.

5. The method according to claim 3, characterized in that, The step of selecting the fill value also includes: If the difference between the highest and second-highest overall scores is less than a preset difference threshold, inconsistent candidate fill values ​​and their data source information will be presented to the user; and Determine the selection made by the user from the presented candidate fill values.

6. The method according to claim 3, characterized in that, The preset initial confidence levels of the interface class structured data source, unstructured parsed data source, and historical data entry data source decrease sequentially.

7. The method according to claim 1, characterized in that, The method further includes: using a second model to generate descriptive content related to the resource to be published, based on the unstructured document and / or user input.

8. The method according to claim 7, characterized in that, The step of generating the description content further includes one or more of the following: Flowchart generation: Generate corresponding business process diagrams based on user input or the business process text description in the unstructured document; Image and text generation: Generate corresponding image content based on user input or the unstructured document; Video generation: Generate corresponding video content based on user input or the unstructured document.

9. The method according to claim 7, characterized in that, Following the step of generating the described content, the following is also included: Receive prompt words input by the user in response to the generated description content; and Based on the prompt words, the generated description content is modified using the second model.

10. The method according to claim 1, characterized in that, Also includes: Monitor the first modification operation related to information elements made by one or more users on the resource template when publishing resources; Based on the number of times the first modification operation is performed, determine whether to update the resource template according to the first modification operation.

11. The method according to claim 10, characterized in that, The step of determining whether to update further includes: Based on a reinforcement learning model, the optimization value of the first modification operation on the resource template is evaluated according to the number of times the first modification operation is executed, wherein the first modification operation is preset to optimize the resource template; and In response to the cumulative evaluation results of the first modification operation reaching a predetermined evaluation threshold, the modification content corresponding to the first modification operation is updated to the resource template.

12. The method according to claim 11, characterized in that, The first modification operation is preset to optimize the resource template, and further includes: assigning a positive reward value to the first modification operation, while assigning a zero or negative value to the operation that is not modified.

13. The method according to claim 11, characterized in that, The reinforcement learning model is based on the Q-Learning reinforcement learning algorithm. A Q-value is defined for the state s of the resource template and the first modification operation a, where state s represents the current state of the resource template, and the following Q-value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a' Q(s',a')-Q(s,a)], Where R represents the immediate reward value for the first modification operation a, s' represents the new state that the resource template enters after executing the first modification operation a, and max a' Q(s',a') represents the maximum Q-value among all first-modification operations in state s', α is the learning rate associated with the Q-value update step size, and γ is the discount factor associated with immediate and future rewards. The method further includes: when the Q value is updated to the predetermined evaluation threshold, updating the resource template according to the modification method of the first modification operation, and setting the Q value to 0.

14. The method according to claim 11, characterized in that, The first modification operation includes any one of the following: adding new information elements, deleting existing information elements, or modifying existing information elements in the resource template.

15. The method according to claim 1, characterized in that, Also includes: According to a preset time period, a data interface is called or a data query script is executed to obtain the latest dynamic data related to the resource. The data interface information or data query script used to obtain the dynamic data of the resource is pre-configured. as well as Update the dynamic data of the published resources using the latest dynamic data obtained.

16. The method according to claim 15, characterized in that, The dynamic data update step further includes: displaying the latest acquired data in the form of a visual chart.

17. The method according to claim 1, characterized in that, Also includes: Based on the received unstructured document corresponding to the published resource, the step of populating the structured fields is triggered to update the information of the published resource.

18. The method according to claim 1, characterized in that, The inherited class template is defined by inheriting all the information elements of the base class template and adding extended information elements corresponding to the type of the resource to be published.

19. The method according to claim 1, characterized in that, The step of parsing the first information using the first model further includes: performing optical character recognition, natural language understanding, and information extraction on the unstructured document.

20. The method according to claim 1, characterized in that, After parsing out the first information, the user confirms the first information before the filling operation is performed based on the first information.

21. An information processing device for resource publishing, characterized in that, The device includes: The template determination module is configured to: in response to a release instruction for a resource to be released, determine a target resource template based on a resource template set, wherein the resource template set includes a base class template with common information elements, and at least one inherited class template with extended information elements that inherits from the base class template; The information parsing module is configured to: based on at least one unstructured document related to the resource to be published, parse first information from the unstructured document using a first model; and The field filling module is configured to fill the structured fields in the target resource template based on the parsed first information for the purpose of publishing the resource to be published.

22. The apparatus according to claim 21, characterized in that, The template determination module is further configured to: Select a class template from a set of resource templates organized in a tree structure, wherein the tree structure indicates the inheritance relationship between the at least one class template and the base class template, as well as between the class templates themselves, wherein each class template inherits directly or indirectly from the base class template. The selected inheritance class template, the inheritance class templates that have inheritance relationships with it, and the base class template are determined as the target resource template.

23. The apparatus according to claim 21, characterized in that, The field filling module includes: The candidate value acquisition unit is configured to acquire candidate fill values ​​from multiple data sources for the same structured field, wherein the data sources include at least two of the following: interface-type structured data sources, unstructured parsed data sources, and historical fill data sources, and each type of data source is set with an initial confidence level. A conflict reconciliation unit is configured to: when candidate imputation values ​​obtained from different data sources are inconsistent, determine a comprehensive score for each candidate imputation value based on a conflict reconciliation algorithm and the initial confidence level; and The candidate value selection unit is configured to select the candidate fill value with the highest comprehensive score as the fill value of the structured field.

24. The apparatus according to claim 23, characterized in that, The conflict reconciliation unit is further configured to determine the overall score based on the following formula: The overall score is calculated as follows: (Initial confidence of the data source × weight α) + (Timestamp confidence × weight β), where α and β are configurable weighting coefficients with values ​​between 0 and 1, and α + β = 1. The timestamp confidence is determined based on the newness of the timestamps of the data corresponding to the candidate fill values.

25. The apparatus according to claim 23, characterized in that, The candidate value selection unit is further configured to: If the difference between the highest and second-highest comprehensive scores is less than a preset difference threshold, inconsistent candidate fill values ​​and their data source information will be presented to the user. as well as Determine the selection made by the user from the presented candidate fill values.

26. The apparatus according to claim 23, characterized in that, The preset initial confidence levels of the interface class structured data source, unstructured parsed data source, and historical data entry data source decrease sequentially.

27. The apparatus according to claim 21, characterized in that, The device further includes: The content generation module is configured to: use a second model to generate descriptive content related to the resource to be published, based on the unstructured document and / or user input.

28. The apparatus according to claim 27, characterized in that, The content generation module is further configured to perform one or more of the following: Flowchart generation: Generate corresponding business process diagrams based on user input or the business process text description in the unstructured document; Image and text generation: Generate corresponding image content based on user input or the unstructured document; Video generation: Generate corresponding video content based on user input or the unstructured document.

29. The apparatus according to claim 27, characterized in that, The device further includes: An interaction optimization module is configured to: receive prompt words input by the user for the generated description content; and modify the generated description content based on the prompt words using the second model.

30. The apparatus according to claim 21, characterized in that, The device further includes: The template update module is configured to: monitor a first modification operation related to information elements performed by one or more users on the resource template when publishing resources; and determine whether to update the resource template based on the number of times the first modification operation is performed.

31. The apparatus according to claim 30, characterized in that, The template update module is further configured to: Based on a reinforcement learning model, the optimization value of the first modification operation on the resource template is evaluated according to the number of times the first modification operation is executed, wherein the first modification operation is preset to optimize the resource template; and In response to the cumulative evaluation results of the first modification operation reaching a predetermined evaluation threshold, the modification content corresponding to the first modification operation is updated to the resource template.

32. The apparatus according to claim 31, characterized in that, The first modification operation is preset to optimize the resource template, and further includes: assigning a positive reward value to the first modification operation, while assigning a zero or negative value to the operation that is not modified.

33. The apparatus according to claim 31, characterized in that, The reinforcement learning model is based on the Q-Learning reinforcement learning algorithm. A Q-value is defined for the state s of the resource template and the first modification operation a, where state s represents the current state of the resource template, and the following Q-value update formula is used: Q(s,a)←Q(s,a)+α[R+γ*max a' Q(s',a')-Q(s,a)], Where R represents the immediate reward value for the first modification operation a, s' represents the new state that the resource template enters after executing the first modification operation a, and max a' Q(s',a') represents the maximum Q-value among all first-modification operations in state s', α is the learning rate associated with the Q-value update step size, and γ is the discount factor associated with immediate and future rewards. The template update module is further configured to: when the Q value is updated to the predetermined evaluation threshold, update the resource template according to the modification method of the first modification operation, and set the Q value to 0.

34. The apparatus according to claim 31, characterized in that, The first modification operation includes any one of the following: adding new information elements, deleting existing information elements, or modifying existing information elements in the resource template.

35. The apparatus according to claim 21, characterized in that, The device further includes: The data update module is configured to: call a data interface or execute a data query script according to a preset time period to obtain the latest dynamic data related to the resource, wherein the data interface information or data query script used to obtain the dynamic data of the resource is pre-configured; and use the obtained latest dynamic data to update the dynamic data of the published resource.

36. The apparatus according to claim 35, characterized in that, The data update module is further configured to display the latest acquired data in the form of a visual chart.

37. The apparatus according to claim 21, characterized in that, The device further includes: The information update module is configured to: based on the received unstructured document corresponding to the published resource, trigger the population step of the structured field to update the information of the published resource.

38. The apparatus according to claim 21, characterized in that, The inherited class template is defined by inheriting all the information elements of the base class template and adding extended information elements corresponding to the type of the resource to be published.

39. The apparatus according to claim 21, characterized in that, The information parsing module is further configured to perform optical character recognition, natural language understanding, and information extraction on the unstructured document.

40. The apparatus according to claim 21, characterized in that, The device is further configured such that: after the information parsing module parses the first information, the first information is confirmed by the user, and then the field filling module performs a filling operation based on the first information.

41. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer program instructions that, when executed by a processor, perform the information processing method as described in any one of claims 1 to 20.

42. An electronic device, characterized in that, include: Processor and memory storing computer program instructions; When the processor executes the computer program instructions, it performs the information processing method as described in any one of claims 1 to 20.

43. A computer program product comprising a computer program that, when executed by a processor, performs the information processing method according to any one of claims 1 to 20.

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