A method and system for constructing a SaaS self-service mall

Abstract

This application relates to a method and system for constructing a SaaS-based self-service e-commerce platform, comprising the following steps: obtaining requirement information and parameter information, and determining business form characteristics and interaction characteristics; determining a basic framework template based on the business form characteristics, and determining several interactive functional components based on the interaction characteristics; combining the basic framework template and interactive functional components, and generating a corresponding e-commerce platform instance configuration; instantiating and generating an e-commerce platform preview instance based on the e-commerce platform instance configuration, and providing it to the target object for verification; responding to a confirmation command, generating an e-commerce platform deployment package, and publishing it to the online operating environment. As can be seen from the above, this application can solve the problems of difficulty in realizing personalized requirements, long development cycles, lack of real-time preview and automated deployment in existing technologies, and has the effects of improving e-commerce platform construction efficiency, reducing development costs, and supporting personalized requirements.

Description

Technical Field

[0001] This application relates to the technical field of e-commerce, and in particular to a method and system for building a SaaS-based self-service online store. Background Technology

[0002] With the popularization of e-commerce, enterprises are increasingly demanding the rapid development of online business through SaaS-based marketplaces. Currently, common SaaS marketplace construction methods mainly fall into two categories: one is to provide standardized fixed templates, which only allow enterprises to make limited configurations and are difficult to meet personalized business needs; the other requires enterprises to invest a lot of technical resources to customize and develop from scratch, which is time-consuming, costly, and complex to maintain.

[0003] While existing technologies offer solutions for building pages using drag-and-drop visual components, these solutions typically focus only on front-end page construction and lack systematic support at the overall architecture level. Specifically, this manifests as an imperfect data isolation mechanism between multiple tenants, which can easily lead to information cross-contamination risks. Furthermore, resource allocation is difficult to dynamically adjust, with fixed quotas often resulting in idle resources or insufficient performance. More critically, existing technologies fail to effectively address the deep collaboration between business logic and user interface, and cannot quickly generate a complete, independently deployable e-commerce instance. These shortcomings lead to low efficiency, high error rates, and difficulty in ensuring system stability for automated e-commerce platform construction, severely hindering the launch and continuous optimization of enterprise businesses. Summary of the Invention

[0004] To address the aforementioned shortcomings, this application provides a method and system for building a SaaS-based self-service e-commerce platform.

[0005] The above-mentioned objective of this application is achieved through the following technical solution: A method for building a SaaS-based self-service e-commerce platform, including the following steps: In response to the e-commerce platform building request from the target object, obtain the corresponding requirement information and parameter information, and determine the business form characteristics and interaction characteristics based on the requirement information and parameter information; Based on the characteristics of the business model, determine the corresponding basic framework template, and based on the interaction characteristics, match several interactive functional components from the preset interactive component library. The matched basic framework templates and interactive functional components are combined, and the corresponding e-commerce instance configuration is generated based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. Instantiate the instance based on the configuration of the e-commerce instance, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification; In response to the confirmation command from the target object, a corresponding e-commerce deployment package is generated based on the verified e-commerce preview instance; The e-commerce platform deployment package is published to the online operating environment allocated to the target audience, thus completing the construction of the SaaS e-commerce platform.

[0006] The second objective of this invention is achieved through the following technical solution: A SaaS-based self-service e-commerce platform building system includes: The feature extraction module is used to respond to the e-commerce platform construction request of the target object, obtain the corresponding demand information and parameter information, and determine the business form characteristics and interaction characteristics based on the demand information and parameter information. The component matching module is used to determine the corresponding basic framework template based on business characteristics, and to match several interactive functional components from the preset interactive component library based on interaction characteristics. The instance configuration generation module combines the matched basic framework template with interactive functional components, and generates the corresponding e-commerce instance configuration based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. The preview instance generation module is used to instantiate the e-commerce instance based on the e-commerce instance configuration, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification. The deployment package generation module is used to generate the corresponding e-commerce deployment package based on the verified e-commerce preview instance in response to the confirmation command of the target object. The e-commerce platform building module is used to publish the e-commerce platform deployment package to the online runtime environment allocated to the target audience, thereby completing the construction of the SaaS e-commerce platform.

[0007] As can be seen from the above, the SaaS-based self-service e-commerce platform construction method and system provided in this application, through steps such as determining business form characteristics and interaction characteristics, matching basic framework templates and interactive function components, combining and generating e-commerce instance configurations and instantiating preview verification, generating deployment packages and publishing to the online environment, can solve the problems of difficulty in realizing personalized needs, long development cycles, lack of real-time preview and deployment automation in existing technologies. It has the effects of improving e-commerce platform construction efficiency, reducing development costs and supporting personalized needs. Attached Figure Description

[0008] Figure 1 This is a flowchart of an embodiment of a SaaS-based self-service e-commerce platform construction method according to this application; Figure 2 This is a schematic diagram illustrating the implementation process of an embodiment of the SaaS-based self-service e-commerce platform construction method of this application; Figure 3This is a flowchart of step S10 in an embodiment of the method for building a SaaS-based self-service e-commerce platform according to this application. Detailed Implementation

[0009] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0010] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0011] In one embodiment, such as Figures 1-2 As shown, this application discloses a method for building a SaaS-based self-service e-commerce platform, which specifically includes the following steps: S10: In response to the e-commerce platform construction request from the target object, obtain the corresponding requirement information and parameter information, and determine the business form characteristics and interaction characteristics based on the requirement information and parameter information; In this embodiment, a SaaS-based self-service e-commerce platform refers to an online e-commerce system provided through a Software as a Service (SaaS) model, allowing users to customize and manage it independently according to their own needs. This online e-commerce system is typically deployed in the cloud, providing services to multiple tenants and ensuring data and business logic isolation between each tenant. Demand information refers to the specific requirements of the target object regarding business functions, industry characteristics, and operating models when building the e-commerce platform. Demand information is typically presented in the form of text descriptions, function lists, or business process diagrams. Parameter information refers to the specific settings provided by the target object regarding interface style, layout preferences, brand identity, and specific function configurations when building the e-commerce platform. Parameter information is typically presented in the form of configuration items, values, or selection options. Business form characteristics refer to the abstract representation extracted from the demand information that describes the core business model and function set of the e-commerce platform. For example, it could be retail, wholesale, service appointment, or content e-commerce. Interaction characteristics refer to the abstract representation extracted from the parameter information that describes the e-commerce platform's user interface and user experience design preferences. For example, it could be simple and flat, immersive experience, social interaction, or data-driven.

[0012] Specifically, in response to the target object's request to build an online store, the system obtains the corresponding requirement information and parameter information, and determines the business model characteristics and interaction characteristics based on the requirement information and parameter information. In one implementation, the target object can submit its online store construction request by filling out an interactive questionnaire. This questionnaire includes open-ended questions about its business type, target customers, core functional requirements, etc., as well as choices about interface colors, layout preferences, navigation styles, etc. After receiving the target object's text and selection data from the interactive questionnaire, the system identifies the business model characteristics and interaction characteristics of the online store to be built based on this text and selection data. In another implementation, the target object can submit a request by uploading a business requirement document or design draft. After receiving the uploaded file from the target object, the system extracts key information based on the file, and then determines the business model characteristics and interaction characteristics.

[0013] S20: Determine the corresponding basic framework template based on the business form characteristics, and match several interactive function components from the preset interactive component library based on the interaction characteristics. In this embodiment, the basic framework template refers to a pre-designed e-commerce skeleton with general business logic and architecture, which can provide the basic structure and core functions of the e-commerce platform and can be selected and expanded according to the characteristics of the business model; the interactive component library refers to a collection and repository of various reusable interactive functional components, including buttons, forms, navigation bars, carousels, product display modules, shopping carts, payment interfaces, etc., used to build the e-commerce platform's user interface and interactive logic; and the interactive functional component refers to an independent functional module component in the interactive component library, used to implement specific user interaction or interface display functions.

[0014] Specifically, a basic framework template is determined based on business characteristics, and several interactive functional components are matched from a pre-defined interactive component library based on interaction characteristics. In one implementation, a list of basic framework templates can be maintained, with each template in the list having a corresponding business characteristics tag. Once the business characteristics of the target e-commerce platform are determined, the basic framework template list can be compared based on these characteristics, and one or more basic framework templates matching the business characteristics tags can be selected. Simultaneously, for matching interactive functional components, an interactive component library can be maintained, with each component having an interaction characteristic tag. Once the interaction characteristics of the target e-commerce platform are determined, several interactive functional components matching the interaction characteristic tags can be filtered from the interactive component library based on these characteristics.

[0015] S30: Combine the matched basic framework template with interactive functional components, and generate the corresponding e-commerce instance configuration based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. In this embodiment, the tenant isolation strategy refers to the rules and technical measures that ensure the independence of data, configuration, and resources among different tenants in a multi-tenant SaaS environment, aiming to protect the data security and system stability of each tenant. A logical isolation unit refers to a logically independent operating environment created for each tenant on shared infrastructure. This logical isolation unit may include independent database instances, file storage space, service processes, or configuration directories. Configuration space refers to a storage area allocated to a specific tenant for storing the personalized configuration data, business rules, and interface settings of that tenant's e-commerce instance. Resource quotas refer to the upper limit of computing resources (such as CPU and memory), storage resources (such as disk space), and network resources (such as bandwidth) allocated to a specific tenant's e-commerce instance, used to limit the tenant's consumption of shared resources and ensure stability. E-commerce instance configuration refers to a set of configuration files describing all the technical details of a specific tenant's e-commerce instance, including basic framework templates, interactive functional components, tenant isolation units, and resource quotas.

[0016] Specifically, the matched basic framework template and interactive functional components are combined, and a corresponding e-commerce instance configuration is generated based on a predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. In one implementation, the interactive functional components can be embedded into the basic framework template according to the interface specifications of the basic framework template and the interactive functional components. After the combination is completed, independent logical isolation units can be allocated to the combined e-commerce framework according to the predefined tenant isolation strategy. For example, a new database instance can be created on a shared database server, and an independent configuration directory and preset resource quotas, such as fixed CPU and memory limits, can be allocated to the database instance.

[0017] S40: Instantiate the e-commerce instance based on the e-commerce instance configuration, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification; In this embodiment, the e-commerce preview instance refers to a runnable and interactive e-commerce version generated based on the e-commerce instance configuration, which is used to enable the target object to perform functional testing, interface verification, and user experience evaluation before formal deployment.

[0018] Specifically, an instantiation is performed based on the e-commerce instance configuration to generate an e-commerce preview instance, and the running status of the e-commerce preview instance is provided to the target object for verification. In one implementation, the code and resources in the generated e-commerce instance configuration can be deployed to a temporary test server to generate an accessible e-commerce preview instance, and the running status of the e-commerce preview instance can be tested, such as whether it starts normally and whether the page is accessible. Furthermore, the running status information of the e-commerce preview instance is sent to the target object for verification.

[0019] S50: In response to the confirmation command of the target object, generate the corresponding e-commerce deployment package based on the verified e-commerce preview instance; In this embodiment, the e-commerce deployment package refers to a software package generated from a verified e-commerce preview instance, containing all necessary code, configuration, and resources. This e-commerce deployment package can be directly deployed to the online runtime environment to complete the official launch of the e-commerce platform.

[0020] Specifically, in response to the confirmation command from the target object, a corresponding e-commerce deployment package is generated based on the verified e-commerce preview instance. In one implementation, after the target object issues a confirmation command, the code, configuration, and related resources of the verified e-commerce preview instance are packaged into a deployable compressed file, i.e., the e-commerce deployment package.

[0021] S60: Publish the e-commerce platform deployment package to the online operating environment allocated to the target audience, completing the construction of the SaaS e-commerce platform.

[0022] In this embodiment, the online operating environment refers to the server cluster, network infrastructure, and related software platform that provide stable and reliable operating services for the SaaS e-commerce platform. The online operating environment typically has high availability, scalability, and security protection capabilities.

[0023] Specifically, the e-commerce platform deployment package is published to the online runtime environment allocated to the target audience to complete the SaaS e-commerce platform construction. In one implementation, the generated e-commerce platform deployment package can be uploaded to a specified directory of the online runtime environment pre-allocated to the target audience, and the deployment script can be executed or the web server can be manually configured to decompress and start the e-commerce platform deployment package, thereby enabling the e-commerce platform to officially go online and run.

[0024] For example, suppose user A wants to build a SaaS-based self-operated online store to sell the handicrafts they make. First, user A submits a store building request through an online form. In the request, user A fills in that their business type is "handicraft retail", their target customers are "consumers who value personalization and originality", and their core functional requirements include "product display", "shopping cart", "online payment", "order management", and "user reviews". At the same time, user A selects a "fresh and natural" interface style in the parameter settings, prefers a "waterfall" product display layout, and uploads their brand logo.

[0025] After receiving the e-commerce platform construction request from user A, the request is processed. By analyzing the requirements submitted by user A, the business model characteristics are identified as "retail e-commerce" and "personalized customized product sales". At the same time, by analyzing the parameter information, the interaction characteristics are identified as "fresh and natural style" and "waterfall flow display".

[0026] Furthermore, based on the identified business model characteristics of "retail e-commerce" and "personalized customized product sales", a basic framework template called "Creative Workshop" is selected from the preset template library. This basic framework template pre-sets the core functions required for retail e-commerce, such as basic product management and order processing. At the same time, based on the interaction characteristics of "fresh and natural style" and "waterfall layout", several interactive functional components are matched from the interaction component library, such as "fresh style product card component", "waterfall layout component", "simple shopping cart component" and "user rating star component".

[0027] Furthermore, the "Creative Workshop" basic framework template is combined with the matched interactive functional components. This combination process ensures that the interactive functional components can be correctly integrated into the basic framework template. Based on this, an independent logical isolation unit is created for user A according to a predefined tenant isolation strategy. Specifically, an independent database instance is allocated to user A on the shared cloud database service to store their product data, order data, and user data. At the same time, an independent file storage namespace is allocated to them to store static resources such as product images and brand logos. In addition, an initial configuration space and resource quota are allocated to user A's e-commerce instance. For example, 2 CPU cores, 4GB of memory, and 100GB of storage space are preset to ensure the basic operating requirements of their e-commerce. Finally, all of the above information is encapsulated into user A's e-commerce instance configuration.

[0028] Furthermore, based on User A's e-commerce instance configuration, an e-commerce preview instance is generated. This e-commerce preview instance can be an accessible website link. User A can use this website link to view the initial form of their e-commerce in a browser and perform functional tests. For example, User A can try adding products, simulating order placement, and viewing the layout and style of the product details page. At the same time, the running status of the e-commerce preview instance is provided to User A for verification, such as the preview link and system health check report.

[0029] Furthermore, after user A fully verifies the store preview instance, confirms that it meets expectations, and issues a confirmation command. In response to the confirmation command, a corresponding store deployment package is generated based on the verified store preview instance. The store deployment package contains all the necessary code, configuration, database scripts, and static resources.

[0030] Finally, the e-commerce platform deployment package is published to the online operating environment allocated to User A. This online operating environment is a highly available and scalable cloud server cluster. After the e-commerce platform deployment package is decompressed and started in the online operating environment, User A's SaaS-based self-operated e-commerce platform can be officially launched and run. User A's customers can access the e-commerce platform via the Internet to browse and purchase products. Through the above process, User A can quickly have a handmade crafts sales e-commerce platform that meets their personalized needs without investing a lot of technical development resources.

[0031] In existing technologies, if user A chooses a standardized template, their personalized needs and interaction preferences are often difficult to meet, resulting in serious homogenization of the online store; while if they choose custom development, they need to invest a lot of time and money to design and implement functions such as product display, shopping cart, and payment from scratch, and subsequent operation and maintenance are complicated.

[0032] The method in this embodiment responds to user A's request to build an online store, obtains and analyzes demand information and parameter information, and then determines business form characteristics and interaction characteristics. Unlike the existing technology that simply provides fixed templates or relies entirely on manual analysis, this method can more accurately capture user A's needs. For example, by identifying "handicraft retail" and "fresh and natural style", it can match the "creative workshop" template and "fresh style product card component", avoiding user A's inefficient selection from a large number of general templates and avoiding designing the basic elements of the online store from scratch.

[0033] Furthermore, this method combines the matched basic framework template with interactive functional components and generates a marketplace instance configuration based on a predefined tenant isolation strategy. In User A's example, not only is the "Creative Workshop" template effectively integrated with "Waterfall Layout Components," but more importantly, it also creates an independent logical isolation unit for User A, such as an independent database instance and file storage namespace, and allocates configuration space and resource quotas. This can solve the problem of insufficient systemic support for multi-tenant isolation and dynamic resource allocation in existing SaaS marketplaces. Compared with the potential for resource mixing or incomplete isolation in existing technologies, this method can ensure User A's data security and system operation stability, and guarantee the independence of its business even on shared infrastructure.

[0034] Furthermore, this method generates a preview instance of the e-commerce platform and provides it to user A for verification, enabling real-time visual verification of the construction process. Before formal deployment, user A can experience the actual effect of the "handicraft retail" e-commerce platform and conduct functional tests, reducing the cost and risk of later modifications in traditional development models. Finally, in response to user A's confirmation instruction, the e-commerce platform deployment package is generated based on the verified preview instance and published to the online runtime environment, enabling the rapid generation of independently deployable e-commerce platform instances. Compared with the complex manual deployment and configuration processes required in existing technologies, this method improves the efficiency and convenience of e-commerce platform construction and lowers the technical threshold for user A.

[0035] In summary, the method of this embodiment provides an efficient, secure, and flexible SaaS-based self-service e-commerce platform construction solution through steps such as intelligent demand analysis, template component matching, tenant isolation configuration, real-time preview, and deployment. It can effectively overcome the problems of existing technologies, such as difficulty in meeting personalized needs, high development costs, complex operation and maintenance, and insufficient multi-tenant support, and has the effect of improving the efficiency of e-commerce platform construction and user experience.

[0036] In one embodiment, such as Figure 3 As shown, step S10 includes: S11: Parse the requirement information and transform it into structured form information; In this embodiment, step S11 aims to convert the original requirement description provided by the target object into a standardized data format that can be recognized and processed. This can be achieved in various ways. For example, natural language processing technology can be used to perform semantic analysis, entity recognition, and intent understanding on the text-based requirement description, extract key information, and fill it into predefined form fields. Alternatively, various preset requirement templates can be provided to guide users to input their requirements by selecting, checking, or filling in specific fields, thereby directly generating structured form information.

[0037] S12: Extract core business items from form information and map them to business form features according to a pre-set classification mapping table; In this embodiment, step S12 aims to identify the core functions and business models of the e-commerce platform from structured demand data. Core business items refer to key functionalities that constitute the basic operational logic of the e-commerce platform, such as product management, order processing, payment settlement, and user management. These core business items can be identified using a rule engine by matching specific field values ​​or keywords in form information. Alternatively, a machine learning model can be trained to identify and extract core business items from structured form data. Furthermore, based on a pre-configured classification mapping table, the extracted core business items are mapped to business form characteristics, such as B2C e-commerce, C2C platform, and content-paid e-commerce, thereby clarifying the overall business positioning of the e-commerce platform.

[0038] S13: Extract interface setting parameters and process setting parameters from parameter information, and determine interaction features according to preset judgment logic rules.

[0039] In this embodiment, step S13 aims to transform the target audience's preferences for the mall's appearance and operation process into specific interactive design features. Interface setting parameters may include color themes, layout styles, navigation methods, etc.; process setting parameters may involve user registration processes, shopping cart checkout steps, message notification mechanisms, etc. A series of configuration options can be preset and provided to the target audience, allowing them to express their preferences by selecting these options; alternatively, a visual configuration tool can be provided, allowing users to drag and adjust interface elements. Furthermore, the extracted parameters are processed through preset judgment logic rules. These judgment logic rules define how different parameter combinations map to specific interactive features, such as whether multilingual support is required, whether social sharing functionality is integrated, and whether a single-page application mode is adopted, thereby guiding the matching of interactive functional components.

[0040] Specifically, the solution in this application achieves precise determination of business form characteristics and interaction characteristics by performing structured parsing of original requirement information and parameter information, extraction and mapping of core business items, and logical judgment of interface and process parameters. Specifically, by parsing the requirement information and transforming it into structured form information, the standardization and consistency of input data are ensured. Based on this, core business items are extracted from the form information and mapped to business form characteristics, thereby accurately identifying the e-commerce platform's business positioning and core functions. Simultaneously, by extracting interface setting parameters and process setting parameters from the parameter information and determining interaction characteristics based on judgment logic rules, it is ensured that the e-commerce platform can meet the personalized needs of the target audience in terms of user experience and operational processes.

[0041] Through the above technical solution, this application can accurately transform the target object's demand information and parameter information into business form characteristics and interaction characteristics that can be identified and processed by the system. This can improve the accuracy of demand analysis in the early stage of e-commerce construction, thereby providing an accurate basis for the selection of basic framework templates and the matching of interactive functional components. At the same time, it also helps to ensure that the constructed SaaS-based self-service e-commerce can more accurately meet the actual business needs and user experience preferences of the target object, shorten the construction cycle, and improve the overall quality and user satisfaction of the e-commerce instance.

[0042] In one embodiment, step S20 includes: S21: Input the business form characteristics into the pre-built template recommendation model, and the template recommendation model outputs template identifiers that match the business form characteristics; In this embodiment, business form characteristics refer to structured information describing the core business logic, operating model, and functional requirements of the target e-commerce platform, such as product types, transaction processes, user roles, and marketing activity requirements. These business form characteristics can be a set of tags based on a predefined classification system or a detailed description of business rules. The template recommendation model is a decision-making model used to identify and recommend the most suitable e-commerce platform basic framework template from a pre-defined template library based on the input business form characteristics. This template recommendation model can be built based on machine learning algorithms (such as collaborative filtering, content recommendation, and deep learning models) or an inference system based on an expert rule base. The template identifier is a unique code or name used to identify a basic framework template, allowing the location of specific template files or configurations. Adaptation means that the recommended template is highly compatible with the business form characteristics in terms of function, structure, and design, effectively supporting the business operations of the target e-commerce platform.

[0043] S22: Based on business characteristics, perform preliminary screening of all interactive functional components in the interactive component library to generate a set of candidate interactive components; In this embodiment, the interaction component library refers to a collection containing various reusable interactive functional components. These interactive functional components can be front-end UI components, back-end service modules, or a combination of both, such as a payment module, a shopping cart module, a product details display module, and a user login / registration module. Preliminary screening refers to filtering the interaction component library based on business characteristics, aiming to narrow down the scope of fine-grained matching. For example, if the business characteristics indicate that online payment needs to be supported, the preliminary screening will retain all payment-related interactive functional components. The candidate interaction component set is a subset of components obtained after preliminary screening, which contains all interactive functional components that may be applicable to the current business characteristics.

[0044] S23: Analyze the interaction features to obtain the core interaction mode, and select several suitable interaction function components from the candidate interaction function component set according to the core interaction mode. The core interaction mode defines the main way for users to exchange data with the mall interface. In this embodiment, interaction features refer to information describing the way users exchange data with the e-commerce interface and their experience preferences, such as navigation methods, data input formats, feedback mechanisms, and response speed requirements. Analyzing the interaction features aims to identify the core interaction patterns between users and the e-commerce interface. The core interaction pattern defines the main way users exchange data with the e-commerce interface, such as whether it is through form submission, real-time chat, drag-and-drop operations, or asynchronous requests for data updates. Selecting several suitable interactive functional components means further selecting the components that best meet the interaction requirements and user experience expectations from the candidate interactive component set based on the analyzed core interaction pattern. For example, if the core interaction pattern is real-time data updates, components that support WebSocket or long polling mechanisms will be given priority.

[0045] S24: Perform pre-validation of the interface and dependencies between the basic framework template corresponding to the template identifier and the selected interactive functional components to ensure that the interactive functional components can be integrated into the framework template.

[0046] In this embodiment, interface and dependency pre-verification refers to a compatibility check performed on the selected basic framework template and interactive functional components before integration. This includes checking whether the API interfaces provided by the components are consistent with the interface specifications reserved in the template, whether the data transmission format matches, whether there are version conflicts, and whether the dependency libraries required by the components for runtime are compatible with the template environment. The purpose of pre-verification is to discover and resolve potential compatibility issues before actual integration, thereby ensuring that interactive functional components can be stably integrated into the basic framework template and reducing the occurrence of integration failures or runtime errors in subsequent development or deployment phases.

[0047] Specifically, the solution proposed in this application introduces a template recommendation model to achieve intelligent template selection based on business characteristics, which can avoid the subjectivity and inefficiency of manual selection. At the same time, through a phased screening and selection mechanism for interactive functional components, it can ensure that the selected interactive functional components not only meet business needs in terms of functionality, but also meet user expectations in terms of interactive experience. More importantly, before combining templates and components, interface and dependency pre-validation can be performed to identify and resolve potential integration problems in advance, thereby improving the success rate and stability of e-commerce platform construction.

[0048] For example, suppose the target entity wants to build a "fresh food e-commerce platform" whose business characteristics include "supporting the sale of multiple categories of fresh food", "having pre-sale function", "supporting cold chain logistics tracking", and "supporting multiple payment methods", and whose interaction characteristics include "users can quickly find products", "product details page needs to display origin traceability information", "order status is updated in real time", and "supporting online customer service consultation", etc.

[0049] First, the business characteristics such as "fresh food e-commerce platform", "pre-sale" and "cold chain logistics" are input into the pre-built template recommendation model. This template recommendation model can analyze the business characteristics based on historical success cases and industry knowledge, and output template identifiers that are compatible with "fresh food e-commerce template V2.1".

[0050] Furthermore, based on the characteristics of the business model, all interactive functional components in the interactive component library are initially screened. For example, based on keywords such as "fresh produce sales", "pre-sale", and "payment", "product category navigation component", "pre-sale management component", "cold chain logistics query component", "online payment component", and "shopping cart component" are selected to form a set of candidate interactive components.

[0051] Furthermore, the interaction features are analyzed to obtain core interaction modes such as "quick search mode," "transparent information display mode," "real-time status push mode," and "instant messaging mode." Based on the obtained core interaction modes, suitable interactive function components are selected from the candidate interaction component set. For example, for "quick search mode," a "search box component" that supports fuzzy search and intelligent association is selected; for "transparent information display mode," a "product details display component" and a "origin traceability information display component" that support mixed text and image layout and video playback are selected; for "real-time status push mode," an "order status update component" based on WebSocket technology is selected; and for "instant messaging mode," an "online customer service chat component" is selected.

[0052] Finally, the interfaces and dependencies of the basic framework template corresponding to "Fresh Food E-commerce Template V2.1" and the selected interactive functional components such as "Search Box Component", "Product Details Display Component", "Origin Traceability Information Display Component", "Order Status Update Component", and "Online Customer Service Chat Component" are pre-verified. For example, it is checked whether the API interface of the "Online Payment Component" is compatible with the payment gateway interface specification of the template, and whether the "Order Status Update Component" depends on the message queue service provided by the template. It is also ensured that the versions of all components are compatible with the template environment. Only after passing all the verifications can it be confirmed that the selected interactive functional components can be seamlessly integrated into the basic framework template.

[0053] Through the above technical solutions, this application can achieve precise matching and integration of basic framework templates and interactive functional components, improving the automation and reliability of SaaS e-commerce platform construction. Furthermore, through a template recommendation mechanism based on pre-built models, it can ensure that the selected template is highly compatible with the business needs of the target e-commerce platform. The step-by-step screening and pre-verification process of interactive components can effectively avoid development delays and system instability caused by component incompatibility or integration conflicts, making the e-commerce platform construction process more efficient, thereby providing the target audience with a high-quality and reliable SaaS e-commerce platform solution.

[0054] In one embodiment, the template recommendation model includes a feature parsing layer, an association matching layer, a decision optimization layer, and an identifier output layer. Step S21 includes: S211: The feature parsing layer performs structured decomposition of business form features to generate a structured feature set including feature values ​​of multiple dimensions; In this embodiment, the template recommendation model refers to a model used to intelligently recommend the most suitable basic framework template for the target object's business form. The template recommendation model can be implemented based on machine learning algorithms, expert system rules, or a combination of both, aiming to improve the accuracy and efficiency of template selection. The template recommendation model includes a feature parsing layer, an association matching layer, a decision optimization layer, and an identifier output layer. The feature parsing layer is used to process the original business form features and transform them into a structured data format that can be recognized and processed by a computer. For example, semantic analysis of text descriptions can be performed using natural language processing technology, or the user-selected tags can be mapped to standardized feature values ​​through a preset rule engine, thereby generating a structured feature set including multi-dimensional feature values. The structured feature set refers to the business form features after parsing and normalization, usually represented by key-value pairs, vectors, or graph structures, which facilitates computer processing and analysis.

[0055] S212: The association matching layer calculates the matching degree between the structured feature set and the business dimension graph corresponding to each template in the pre-defined template library based on the predefined business logic graph, and generates an initial matching sequence; In this embodiment, the association matching layer is used to perform preliminary matching degree calculations based on the structured feature set output by the feature parsing layer and the business dimension graphs associated with each template in the pre-set template library. The association matching layer can utilize graph matching algorithms, semantic similarity calculations, or rule-based matching logic to quantify the degree of fit between business form features and different templates. The predefined business logic graph refers to a graph structure knowledge base describing the logical relationships and dependencies between different business domains and functional modules, such as the dependency relationship between "product management" and "order management" in e-commerce. The business dimension graph refers to a graph structure representation of the business functions, industry characteristics, and other dimensional information supported by the template, used for matching with the structured feature set. Typically, each template is associated with a pre-set business dimension graph. The matching degree is a quantitative indicator that measures the degree of fit between business form features and templates, and can comprehensively consider factors such as functional coverage and industry adaptability. The initial matching sequence is a list obtained by sorting the templates according to the preliminary matching degree.

[0056] S213: The decision optimization layer adjusts the weights of each matching item in the initial matching sequence based on historical deployment data, filters out matching items below the preset success threshold, and generates an optimized recommendation sequence. In this embodiment, the decision optimization layer is used to further improve the accuracy and usability of the matching results. Specifically, the decision optimization layer introduces historical deployment data to adjust the weights of each matching item in the initial matching sequence generated by the association matching layer and filters out matching items that do not meet preset success criteria. For example, this can be achieved through regression or classification models, adjusting the matching degree based on indicators such as historical success rate and user satisfaction to ensure the reliability of the recommendation results. Historical deployment data refers to data such as the success rate of past mall deployments, user feedback, and performance, used to guide decision optimization. Weight adjustment refers to adjusting the importance or priority of matching items based on historical data to reflect their performance in actual applications. The preset success threshold is the minimum standard for judging whether the matching result is acceptable; it can usually be preset according to the actual scenario and actual needs. The optimized recommendation sequence refers to the template recommendation list obtained after correction and filtering. The template identifier refers to the code or name that uniquely identifies a basic framework template.

[0057] S214: The identifier output layer determines template identifiers that are adapted to business form characteristics based on the optimized recommendation sequence.

[0058] In this embodiment, the identifier output layer is used to determine and output the template identifier that best matches the business form characteristics based on the optimized recommendation sequence generated by the decision optimization layer. The identifier output layer can use a sorting algorithm to select the highest-ranked template, or provide multiple alternative templates for the user to choose from when specific conditions are met. The business form characteristics refer to comprehensive information describing the business attributes, functional requirements, industry characteristics, etc. of the target object. For example, it can be "e-commerce retail", "content payment", "community group buying", etc.

[0059] Specifically, the proposed solution designs the template recommendation model as a multi-layered architecture model comprising a feature parsing layer, an association matching layer, a decision optimization layer, and an identifier output layer, thereby achieving refined processing and multi-dimensional matching of business form features. Specifically, firstly, the feature parsing layer structurally decomposes the input business form features, transforming them into a structured feature set that the model can understand and process. Secondly, the association matching layer, based on a predefined business logic graph, matches the structured feature set with the business dimension graphs of each template in the template library, generating an initial matching sequence to ensure preliminary functional and business logic fit. Thirdly, the decision optimization layer, by introducing historical deployment data, adjusts and filters the initial matching sequence, effectively avoiding the recommendation of unsuitable or poorly performing templates, thus improving the reliability and usability of the recommendation results. Finally, the identifier output layer determines the most suitable template identifier based on the optimized recommendation sequence.

[0060] For example, suppose the target entity wants to build a "fresh food e-commerce mall," whose business characteristics include "supporting multiple suppliers," "requiring cold chain logistics management," and "supporting community group buying." First, the feature parsing layer will perform a structured decomposition of the business characteristics to generate a structured feature set. Further, the association matching layer will compare this structured feature set with the business dimension graphs of all templates in the template library based on a predefined e-commerce business logic graph. For example, template A may support "B2B2C model" and "product management, order management, and payment," but lacks "cold chain logistics" and "community group buying" functions; template B, on the other hand, fully supports these features. These functions are present; at this point, the association matching layer calculates that template B has a higher matching degree and generates an initial matching sequence; further, the decision optimization layer corrects the initial matching sequence based on historical deployment data. For example, if historical data shows that templates supporting cold chain logistics have a higher success rate in the fresh food e-commerce field, or if a template caused user churn due to performance issues of the cold chain management module in past deployments, the decision optimization layer will adjust the weights of the matching items in the initial matching sequence accordingly and filter out templates below the preset success threshold; finally, the identification output layer determines and outputs the most suitable template identification based on the optimized recommendation sequence, for example, recommending template B.

[0061] Through the above technical solution, this application can accurately identify the business form characteristics of the target object, and recommend the most suitable basic framework template based on multi-dimensional matching and historical data optimization. This not only solves the problems of insufficient accuracy and poor adaptability that may exist in traditional template recommendation, but also improves the automation level and success rate of e-commerce platform construction, thereby improving the user experience of the target object.

[0062] In one embodiment, step S212 includes: S2121: Extract several key business nodes and their relationships from the predefined business logic graph, and construct the target business logic subgraph corresponding to the structured feature set; In this embodiment, step S2121 aims to concretize the abstract business form features into an analyzable graph structure. The predefined business logic graph refers to a knowledge graph or flowchart containing various business entities and their interactions. By extracting parts related to the current structured feature set from the business logic graph, a more accurate target business logic subgraph can be formed. This target business logic subgraph can intuitively reflect the core business processes and data flow of the target marketplace. Furthermore, the extraction process of key business nodes and node relationships can be achieved through semantic analysis and keyword matching. Key business terms in the structured feature set are matched with node names in the business logic graph. Then, based on the matched nodes, related nodes and edges are extracted along the preset relationships to construct the target business logic subgraph. Alternatively, a rule engine-based approach can be used, with multiple preset rules, to generate the target business logic subgraph.

[0063] S2122: Align and compare the target business logic subgraph with the standard business dimension graph associated with all templates in the template library, and calculate the composite matching degree. The composite matching degree includes node coverage, relationship path consistency and business constraint satisfaction. In this embodiment, step S2122 aims to semantically compare the business logic subgraph of the target e-commerce platform with the standard business dimension graph of each template. The composite matching degree refers to a multi-dimensional indicator that comprehensively considers the completeness of business logic (node ​​coverage), the similarity of processes (relationship path consistency), and the success potential of actual business operation (business constraint satisfaction). Node coverage can be measured by calculating the proportion of nodes in the target business logic subgraph that exist in the standard business dimension graph. Relationship path consistency can be calculated by comparing the similarity of paths between identical nodes in the two graphs, for example, using graph edit distance or a variant of the graph isomorphism algorithm. Business constraint satisfaction... Sustainability can be assessed by defining a series of success rate influencing factors for each business node based on historical deployment data, and then evaluating the degree of conformity between the target business logic subgraph and the template on these factors. In addition, node coverage can also use Jaccard similarity coefficient or cosine similarity to measure the similarity between two graph node sets. Relationship path consistency can be achieved by converting the graph into an adjacency matrix and then calculating the similarity between the matrices, or by using graph embedding technology to map the graph to a low-dimensional vector space for distance calculation. Business constraint satisfaction can be achieved by building a machine learning model based on historical data, inputting the features of the target business logic subgraph and the features of the template, and predicting its success probability under specific business constraints.

[0064] S2123: The composite matching degree of each template is weighted and integrated with its historical average deployment success rate to generate a comprehensive matching degree; In this embodiment, step S2123 aims to combine the business logic matching degree with the template's performance in actual deployment to form a more comprehensive evaluation index. The composite matching degree reflects the template's functional and structural fit with the requirements, while the historical average deployment success rate reflects the template's reliability and stability in practical applications. Through weighted fusion, it can be ensured that the recommended template not only meets the requirements theoretically but also has a high success rate in practice. The weighted fusion can be linear, i.e., comprehensive matching degree = w1 × composite matching degree + w2 × historical average deployment success rate, where w1 and w2 are preset weights, and w1 + w2 = 1. The weights can be set based on experience or optimized through machine learning models. Alternatively, non-linear weighted fusion can be used, for example, by converting the composite matching degree and historical average deployment success rate using a Sigmoid or Tanh function before fusion, or by using models such as decision trees or random forests to learn how to effectively fuse these two indicators.

[0065] S2124: Sort all templates in the template library in descending order based on the overall matching degree to generate an initial matching sequence.

[0066] In this embodiment, step S2124 aims to prioritize all available templates based on the previously calculated comprehensive matching degree. Descending order means that templates with higher comprehensive matching degrees are placed earlier in the sequence, making them more likely to be recommended to the target object. The initial matching sequence is the basis for further filtering and correction by the decision optimization layer. This ranking can be achieved using a standard ranking algorithm for templates and their corresponding comprehensive matching degrees; alternatively, templates and comprehensive matching degrees can be stored in a priority queue, and the top-ranked templates can be obtained by maintaining the ranking status.

[0067] Specifically, in the SaaS-based self-service e-commerce platform construction method, to more accurately recommend basic framework templates that match business characteristics, the association matching layer, after receiving the structured feature set output by the feature parsing layer, first extracts several key business nodes and node relationships from the predefined business logic graph based on the business requirements reflected by the structured feature set. This constructs a target business logic subgraph focusing on the core business processes of the target e-commerce platform. This target business logic subgraph graphically depicts the business architecture of the target e-commerce platform. Further, the association matching layer aligns and compares the target business logic subgraph with the standard business dimension graphs associated with all pre-set templates in the template library. This comparison is typically multi-dimensional, calculating composite matching degrees to consider node coverage, relationship path consistency, and business constraint satisfaction. Among these, the completeness of the business logic (node ​​coverage) refers to the degree to which the key business nodes in the target business subgraph are reflected in the template graph; the similarity of the business processes (relationship path 1) is also considered. The composite matching degree is calculated as follows: **Composite Matching Degree:** This refers to the degree of alignment between the business process path in the target business subgraph and the corresponding path in the template graph. **Business Constraint Satisfaction Degree:** This refers to the success rate influencing factors associated with specific business nodes, derived from historical deployment data statistics. By calculating the various feature parameters in the composite matching degree, the potential of a template to satisfy specific business constraints in actual business operations can be assessed. To further enhance the practicality and reliability of the recommendations, the association matching layer weights and fuses the composite matching degree with the historical average deployment success rate of each template to generate a comprehensive matching degree. This comprehensive matching degree considers not only the functional and structural fit of the template but also its performance and stability in actual applications. Finally, the association matching layer sorts all templates in the template library in descending order based on the comprehensive matching degree, thereby generating an initial matching sequence. The templates in this initial matching sequence are sorted according to a comprehensive consideration of their fit with the target business characteristics and actual deployment success rate, providing a more insightful and guiding recommendation basis for the decision optimization layer.

[0068] Through the above technical solution, this application can improve the accuracy and practicality of template recommendation. Traditional template recommendation may only be based on simple keyword matching or feature tag comparison, which can easily lead to recommended templates that seem functionally compatible but are mismatched in terms of actual business processes, data flow, or specific business constraints, thereby increasing the cost of customized development and deployment risks. This application constructs a target business logic subgraph from a predefined business logic graph and performs multi-dimensional alignment and comparison with the standard business dimension graph of the template. It can evaluate the template and the target from multiple levels, such as the completeness of business logic, the similarity of processes, and the satisfaction of actual business constraints. The system assesses the fit between business model characteristics to obtain a more insightful composite matching score. Furthermore, it weights and integrates the composite matching score with the historical average deployment success rate of the templates. This ensures that the recommendation results not only consider theoretical functional matching but also the reliability and stability of the templates in actual operation, effectively avoiding the recommendation of invalid templates. Through this comprehensive evaluation mechanism, it ensures that the templates in the generated initial matching sequence are not only highly adaptable in business logic but also have a high probability of success in actual deployment. This provides a more reliable and efficient foundation for building a SaaS e-commerce platform for target users, reducing their trial-and-error costs and development cycle.

[0069] In one embodiment, step S20 further includes: S201: When the interaction feature fails to match the interaction function component, identify the reason for the failure. The reasons for the failure include missing required functions, incompatible interfaces, and conflicts with the selected components. In this embodiment, identifying the cause of failure refers to analyzing the reasons for unsuccessful matching when attempting to match interactive features with interactive functional components using preset rules or analysis mechanisms. For example, for missing required functions, the explicit or implicit functional requirements in the interactive features can be compared with the list of functions declared or actually provided by the components in the interactive component library. If no component can meet the specific function, it is determined that the function is missing. Another approach is to perform semantic analysis on the interactive features, decompose them into a series of atomic functional points, and then check whether the interactive components cover these atomic functional points. For interface incompatibility, the API interface definition (such as data type, parameter list, communication protocol, etc.) of the component to be matched can be checked to see if it conforms to the interface specification of the basic framework template or the selected component. This can be pre-checked using static code analysis tools or dynamically verified in a simulated integration environment. For conflicts with selected components, a component dependency graph or conflict matrix can be maintained. When attempting to introduce a new component, it can be checked whether it conflicts with existing components in terms of resource consumption, data model, business logic, etc. For example, two components may attempt to modify the same core data or have different response logic for the same user behavior.

[0070] S202: Map the failed-matching interaction features together with the business form features to a pre-built e-commerce knowledge graph. Through graph reasoning, perform semantic enhancement and standardization description on the failed-matching interaction features to generate enhanced functional requirement descriptions. In this embodiment, the interaction features that fail to match are mapped together with the business form features to a pre-built e-commerce knowledge graph. Semantic enhancement and normalization of the interaction features that fail to match are then performed through graph reasoning. This means understanding and optimizing the interaction features that lead to the failure to match using an e-commerce knowledge graph containing professional knowledge of the e-commerce domain. The e-commerce knowledge graph can be a complex network composed of nodes (representing business entities such as "products," "users," "orders," "payments," "marketing activities," etc.) and edges (representing relationships between entities such as "products belong to a category," "users place orders," "orders contain products," "payments complete orders," etc.). When the interaction features that fail to match (e.g., "complex promotional rules") and the business form... When features (such as "B2B wholesale") are input into an e-commerce knowledge graph, the corresponding graph reasoning engine performs association analysis and logical inference within the graph based on these inputs. For example, for "complex promotional rules" under "B2B wholesale," it may infer that they involve more specific business concepts such as "tiered pricing," "customer level discounts," and "bulk purchase discounts," and further associate them with technical implementation models such as "rule engine" and "price calculation service." Through this reasoning, the original, potentially vague, or incomplete interaction features can be transformed into more specific descriptions of enhanced functional requirements. These enhanced functional requirements not only include the original requirements but also incorporate domain knowledge, providing potential alternatives or more detailed functional decompositions.

[0071] S203: Based on the description of enhanced functional requirements, perform semantic retrieval and matching again in the interactive component library to match alternative interactive functional components.

[0072] In this embodiment, based on the enhanced functional requirement description, semantic retrieval and matching are performed again in the interactive component library to match alternative interactive functional components. This means that after obtaining the functional requirement description which has been enhanced and normalized by the knowledge graph, semantic analysis technology is used to search in the interactive component library. Semantic retrieval can employ natural language processing technology to convert both the enhanced functional requirement description and the component's functional description into vector representations. Then, the most matching component is found by calculating the similarity between the vectors. For example, even if the component's name does not completely match a word in the enhanced functional requirement description, it can still be retrieved if its functional description is highly related semantically. Matching alternative interactive functional components means not only looking for components that completely meet the original requirements, but also looking for components that can satisfy the enhanced functional requirement description through combination, configuration, or minor modifications. This may include components that are functionally equivalent but have different implementation methods, or multiple component groups that can satisfy some of the enhanced requirements.

[0073] Specifically, when the initial matching of interaction features and interactive functional components fails, the specific reasons for the failure are first identified. The failed interaction features, along with business form features, are then input into a pre-built e-commerce knowledge graph. This e-commerce knowledge graph, as a carrier of domain expert knowledge, can perform semantic understanding and reasoning on the original, potentially ambiguous, or incomplete interaction features. Through graph reasoning, the interaction features can be transformed into more specific descriptions of enhanced functional requirements, effectively compensating for any shortcomings in the initial feature descriptions, and incorporating contextual information from the business scenario. Finally, based on the enhanced requirement descriptions, semantic retrieval and matching are performed in the interaction component library to identify functionally equivalent or substitutable interactive functional components, even if these components were not discovered in the initial matching stage.

[0074] For example, suppose a target object requests the construction of a SaaS e-commerce platform, whose business model is "cross-border e-commerce," and whose interaction features include "multilingual product detail pages" and "automatic currency switching based on user IP." During the initial matching process, no single component in the interaction component library can fully meet the requirement of "automatic currency switching based on user IP," or the interface of existing components is incompatible with the selected basic framework template. At this point, the reasons for failure are identified, such as "missing required functionality" (lack of automatic currency switching logic) and "interface incompatibility" (existing currency conversion component does not match the framework API). The "automatic currency switching based on user IP" (the interaction feature that failed to match) and "cross-border e-commerce" (business model) are then matched. The morphological features are mapped together to the e-commerce knowledge graph, enabling the e-commerce knowledge graph to enhance the semantics of "automatically switching currencies based on user IP" into an enhanced functional requirement description of "integrating third-party exchange rate services, dynamically adjusting product price display based on user geographic location information, and supporting settlement in multiple mainstream currencies." Based on this enhanced functional requirement description, a semantic search is performed again in the interactive component library. This semantic search may match a combination of a "third-party exchange rate API integration component," a "geographic location recognition and currency display component," and a "multi-currency payment gateway interface component." These matched components together constitute replaceable interactive functional components, thus successfully meeting the original requirements of the target object.

[0075] Through the above technical solutions, this application can effectively solve the problem of construction interruption or low efficiency caused by the failure to match interactive features and interactive functional components during the construction of SaaS-based self-service e-commerce platforms. Specifically, by identifying the reasons for failure, using e-commerce knowledge graphs for semantic enhancement and standardized description, and performing semantic retrieval and matching, the success rate and accuracy of component matching can be improved. This not only reduces the need for manual intervention and improves the efficiency of e-commerce platform construction, but also allows for more flexible adaptation to diverse and complex business needs, thereby improving the automation level and user satisfaction of the SaaS-based self-service e-commerce platform construction method.

[0076] In one embodiment, the logical isolation unit includes a logical database instance, a file storage namespace, and a service instance identifier. Step S30 includes: S31: Perform dynamic dependency analysis on the integration interface of the basic framework template and the service interface provided by the interactive functional components, identify and resolve potential call conflicts between the integration interface and the service interface, and generate a sequence and dependency graph. In this embodiment, dynamic dependency analysis refers to detecting the compatibility, data format, parameter requirements, and call order of the interfaces between the basic framework template and interactive functional components at runtime or before deployment. This aims to identify and resolve potential integration problems in advance. This can be achieved by scanning interface definitions using static code analysis tools or by performing interface call tests in a simulated environment to collect interface call chains and data flow information. Identifying and resolving potential call conflicts involves discovering issues such as interface name conflicts, parameter type mismatches, version incompatibility, and resource contention during dependency analysis, and providing solutions or adjustment suggestions. This can be achieved through interface adapter pattern conversion, selecting compatible component versions through version management strategies, or resolving resource contention through configuration adjustments. Generating a sequence and dependency graph is a visual or structured data representation that clearly shows the call order, data flow, and dependencies between the basic framework template and all interactive functional components. Its purpose is to provide guidance for component injection and integration. This can be represented by a directed acyclic graph, where nodes represent interfaces or components, and edges represent calls or dependencies.

[0077] S32: Based on the sequence and dependency graph, interactive functional components are injected into the basic framework template, and version compatibility checks and runtime environment pre-verification are performed during the injection process to form a combined e-commerce framework; In this embodiment, injection refers to integrating the code, configuration, or service references of interactive functional components into the basic framework template, making them part of the combined e-commerce framework. Its purpose is to achieve componentization and modularization, which can be achieved through dependency injection frameworks, plugin mechanisms, code generation, compile-time integration, etc. Version compatibility checking refers to checking whether there are any conflicts between the version of the selected interactive functional components and the versions of the basic framework template and other injected components, confirming whether they can work together. Its purpose is to avoid runtime errors caused by version mismatch. It can be achieved by comparing the version numbers in the component list or by querying a predefined compatibility matrix. Runtime environment pre-verification refers to checking whether the target runtime environment meets the minimum requirements of all components before deploying the combined e-commerce framework, such as operating system version, library dependencies, environment variables, port usage, etc. Its purpose is to ensure that the combined e-commerce framework can start and run smoothly. It can be achieved by executing environment checks with automated scripts or by encapsulating the runtime environment through containerization technology.

[0078] S33: Based on the tenant isolation strategy, create independent logical database instances, file storage namespaces, and service instance identifiers for the combined e-commerce framework; In this embodiment, a logical database instance refers to providing each tenant with an independent database instance or creating an independent logical partition in a shared database, such as an independent schema or table prefix, to ensure data isolation and security. This can be achieved through database virtualization technology, or by assigning each tenant an independent database user and permissions in a multi-tenant database, allowing them to access only their own data. A file storage namespace refers to allocating an independent storage area or directory structure for each tenant to store their e-commerce-related file resources such as images, documents, and videos, preventing file confusion or unauthorized access between different tenants. This can be achieved by creating an independent bucket or prefix for each tenant using object storage services, or by creating an independent root directory for each tenant in a shared file system and implementing access control. A service instance identifier refers to assigning a unique identifier to each tenant's e-commerce instance to distinguish service requests and resource usage from different tenants, ensuring that requests are routed to the correct tenant instance and enabling independent monitoring and management. This can be achieved by assigning a unique tenant ID to each tenant's service instance in a microservice architecture, or by giving each tenant's container instance a unique label in a containerized deployment.

[0079] S34: Identify the resource consumption characteristics of interactive functional components, and allocate initial resource quotas and elastic scaling strategies based on the resource consumption characteristics; In this embodiment, identifying the resource consumption characteristics of interactive functional components refers to analyzing the resource usage patterns and peak demands of each interactive functional component during runtime, such as CPU, memory, storage, and network bandwidth. Its purpose is to provide a basis for accurate resource allocation. This can be achieved through historical runtime data analysis, performance testing, or resource requirement declarations provided by component developers. Allocating initial resource quotas refers to allocating sufficient resources to each tenant's e-commerce instance to ensure its normal operation based on the identified resource consumption characteristics. Its purpose is to ensure that newly created e-commerce instances have sufficient resources to start and run. This can be achieved by configuring container CPU / memory limits, virtual machine specifications, and database connection limits. The elastic scaling strategy defines the rules for automatically adjusting the resource quotas of e-commerce instances when their resource demands change, such as during traffic peaks. Its purpose is to cope with business load fluctuations and optimize resource utilization. This can be achieved by triggering automatic scaling rules based on indicators such as CPU utilization, memory utilization, and request queue length, such as adding virtual machine instances or adjusting the number of container replicas.

[0080] S35: Encapsulate the combined e-commerce framework, logical isolation units, and their associated initial resource quotas and elastic scaling strategies into an e-commerce instance configuration.

[0081] In this embodiment, encapsulation as e-commerce instance configuration refers to packaging all the information about the combined e-commerce framework, tenant isolation settings, resource allocation, and scaling strategies into a unified and deployable configuration unit. Its purpose is to simplify the deployment, management, and maintenance of e-commerce instances. This can be achieved through configuration files in formats such as JSON and YAML, or through unified management via a configuration management system.

[0082] Specifically, the solution in this application performs dynamic dependency analysis on the integration interfaces of the basic framework template and the service interfaces provided by the interactive functional components. This effectively identifies and resolves potential call conflicts and generates a clear sequence and dependency graph, ensuring seamless integration between components. Furthermore, when injecting interactive functional components into the basic framework template, version compatibility checks and runtime environment pre-verification are performed, further guaranteeing the stability and reliability of the combined e-commerce framework. Simultaneously, based on tenant isolation strategies, independent logical database instances, file storage namespaces, and service instance identifiers are created for the combined e-commerce framework, achieving data, file, and service isolation between tenants and avoiding resource misuse and security risks. In addition, by identifying the resource consumption characteristics of interactive functional components and allocating initial resource quotas and elastic scaling strategies accordingly, resource allocation becomes more precise and efficient, avoiding resource waste and responding to dynamic changes in business load. Finally, the combined e-commerce framework, logical isolation units, and their associated initial resource quotas and elastic scaling strategies are encapsulated as e-commerce instance configurations, significantly simplifying the deployment and management process of SaaS-based e-commerce platforms.

[0083] Through the above technical solutions, this application can effectively solve the compatibility problem in the component integration process, ensure the stable integration of different interactive functional components with the basic framework template, and at the same time, through the tenant isolation strategy, ensure the data security and resource independence between each e-commerce instance. In addition, based on the identification of component resource consumption characteristics, it can realize the reasonable allocation of initial resource quotas and the dynamic adjustment of elastic scaling strategies, thereby optimizing resource utilization, avoiding resource waste and performance bottlenecks, and improving the efficiency, stability and scalability of SaaS e-commerce construction.

[0084] In one embodiment, after step S34, the method further includes: S341: Generate simulated traffic based on a preset typical user behavior model, and conduct stress testing and behavior analysis on the combined e-commerce framework through simulated traffic; In this embodiment, step S341 aims to evaluate the performance of the constructed combined e-commerce framework by simulating real user behavior. Here, the typical user behavior model refers to the abstraction and quantification of the behavioral patterns of the target e-commerce potential user group, such as user browsing paths, product search frequency, and order conversion rates. Simulated traffic refers to a large number of virtual user requests generated based on the typical user behavior model, used to simulate concurrent access and operations in the real world. Stress testing refers to evaluating the stability, response time, throughput, and resource utilization of the combined e-commerce framework under extreme conditions by applying simulated traffic exceeding normal loads. Behavioral analysis refers to monitoring the combined e-commerce framework's response to different... Responses to user behavior patterns help identify potential interaction problems or performance bottlenecks. This step can be achieved by defining user behavior scripts using professional performance testing tools (such as JMeter and LoadRunner) and configuring parameters such as the number of concurrent users and loop counts to generate simulated traffic. These performance testing tools can simulate HTTP / HTTPS requests and record metrics such as response time and error rate. Alternatively, user behavior prediction models based on machine learning or statistical analysis can be used in conjunction with historical data to generate more realistic simulated traffic patterns, such as simulating peak periods or flash sales, and then automating stress testing through custom testing frameworks or scripts.

[0085] S342: Based on the results of stress testing and behavioral analysis, predict the performance bottlenecks and data access hotspots of the combined e-commerce framework in a real operating environment; In this embodiment, step S342 aims to analyze the data collected from stress testing and behavioral analysis to identify potential performance bottlenecks and concentrated data access areas during actual operation. A performance bottleneck refers to a point where, under a specific load, a component or resource (such as CPU, memory, network bandwidth, database I / O, etc.) reaches its limit, causing an overall performance degradation. Data access hotspots refer to data areas or database tables that are frequently accessed, read, or modified during the operation of the online store. This step can locate performance bottlenecks by analyzing various indicators in the stress test report (such as CPU utilization, memory usage, database query time, network latency, etc.), combined with system logs and monitoring data, and using statistical methods or expert experience. For example, consistently high CPU utilization may indicate computationally intensive tasks, while slow database query logs may reveal database bottlenecks. Alternatively, data mining techniques can be used to perform cluster analysis or association rule mining on user access logs and database query logs collected during behavioral analysis to identify specific products, user data, or business modules with high access frequency and large data volumes, thus defining them as data access hotspots.

[0086] S343: Based on the performance bottleneck, inject the corresponding performance optimization configuration into the mall instance configuration. The performance optimization configuration includes one or more of the following: static caching rules, database connection pool parameters, and distributed lock strategies. In this embodiment, step S343 aims to integrate specific optimization measures into the configuration of the e-commerce instance to improve system performance, targeting predicted performance bottlenecks. Performance optimization configuration refers to a set of preset, adjustable parameters or strategies designed to alleviate or eliminate performance bottlenecks. Static caching rules refer to pre-generating static HTML files or storing frequently accessed content in the cache to reduce dynamic rendering and database queries, thereby accelerating response speed. Database connection pool parameters refer to the configuration of the pooling mechanism managing connections between the application and the database; proper configuration can reduce the overhead of establishing and closing database connections, improving database access efficiency. Distributed lock strategies refer to strategies used in distributed systems to coordinate access to shared resources by multiple processes or threads, preventing data inconsistency or concurrency conflicts. This step can be implemented by modifying the e-commerce instance's configuration file (such as XML, YAML, or JSON files) or by dynamically loading the configuration module at startup programmatically. For example, for static caching, Nginx or CDN rules can be configured; for the database connection pool, parameters of the application server or ORM framework can be adjusted.

[0087] Furthermore, the distributed lock strategy is a dynamically configured strategy based on predicted performance bottlenecks (such as high-concurrency write operation interfaces). For example, the distributed lock strategy may specifically include: Redis-based inventory deduction lock: When "inventory deduction" is predicted to be a high-frequency concurrent operation, a distributed lock strategy based on Redis is injected into the configuration. This strategy stipulates that before executing the deduction operation, a lock with a millisecond-level timeout must be acquired using the product SKU as the key. This ensures that in a distributed environment, only one request can execute the inventory modification of the product at any given time, thereby completely preventing overselling.

[0088] Order status update lock based on database row locks: When "order status update" (such as changing from "pending payment" to "paid") is predicted to be a critical concurrent path, a strategy based on database row locks is injected. This strategy ensures that when the core order status is updated, the database transaction will lock the row data, preventing other transactions from modifying it at the same time, thus guaranteeing the strict consistency of the order state machine.

[0089] The ZooKeeper / etcd-based global configuration release lock: When it is predicted that the "global configuration (such as shipping template, promotion rules) release" function in the e-commerce backend may be operated by multiple administrators at the same time, a distributed lock strategy based on a strong consistency coordination service is injected. This strategy ensures that the global configuration release operation has global uniqueness and sequence in the cluster, avoiding configuration chaos or overwriting caused by concurrent release.

[0090] Optimistic locking-based shopping cart merging conflict control: When it is predicted that users will frequently perform "merge shopping carts" and that data version conflicts may occur, an optimistic locking strategy based on data version numbers is injected. This strategy maintains a version number for each shopping cart data item. When updating, the version number is checked. If they are inconsistent, the user is prompted of the conflict and the cart is automatically merged or required to be manually resolved, rather than simply locking and blocking. This approach ensures data correctness while also taking into account user experience and concurrency efficiency.

[0091] In this embodiment, for example, if stress testing predicts that the "flash sale inventory deduction" interface is a performance bottleneck and a data consistency risk point, then the complete configuration items of the above-mentioned "Redis-based inventory deduction lock" strategy will be automatically injected into the generated e-commerce instance configuration, including the Redis connection address, lock key prefix, timeout time, etc. When the e-commerce instance is deployed and launched, its "inventory deduction" service will automatically execute according to this strategy, without the need for developers to manually write relevant lock code, thereby avoiding the overselling problem under high concurrency at the source.

[0092] S344: Based on data access hotspots, inject corresponding data preheating instructions into the mall instance configuration. The data preheating instructions are used to preload hotspot data into the cache when the mall instance starts.

[0093] In this embodiment, step S344 aims to proactively load frequently accessed data into a high-speed cache when the e-commerce instance starts, targeting identified data access hotspots, to reduce latency during the user's first access and improve data reading efficiency. The data preheating instruction refers to a part of the startup script or configuration, instructing specific data loading operations to be performed during the initialization phase. This step can be a startup script that, after the e-commerce instance starts, executes a series of predefined database queries or API calls to populate distributed caches such as Redis and Memcached with the query results. Alternatively, it can be implemented in the e-commerce instance's application code by configuring an initialization module that automatically executes preheating logic when the application starts, such as loading popular product lists and frequently used user configurations into local or distributed caches.

[0094] Furthermore, performance bottlenecks and data access hotspots can be used as dynamic inputs to correlate and optimize the trigger thresholds and scaling rules of elastic scaling strategies.

[0095] This approach uses performance bottlenecks and data access hotspots as dynamic inputs to correlate and optimize the trigger thresholds and scaling rules of elastic scaling strategies, aiming to make these strategies more adaptive. By using predicted performance bottlenecks and data access hotspots as real-time or periodic inputs, the trigger conditions (thresholds) and scaling behaviors (rules) of elastic scaling strategies are dynamically adjusted to ensure more precise resource adjustments based on actual load changes. The trigger threshold refers to the performance metric boundary that determines when to start or stop scaling operations, such as scaling up when CPU utilization reaches 80% and scaling down when it falls below 20%. The scaling rules refer to the specific methods of scaling up or down, such as per... This involves adding or removing several instances, or making linear or non-linear adjustments based on the load curve. This step can use performance bottlenecks and data access hotspots as new monitoring metrics and integrate them with the cloud platform's elastic scaling service. When these metrics reach preset dynamic thresholds, scaling up or down operations are automatically triggered. Alternatively, custom automated operation and maintenance scripts or policy engines can periodically analyze performance bottlenecks and data access hotspots, and dynamically modify the configuration files or API parameters of the elastic scaling policy based on the analysis results. For example, adjusting the scaling threshold for CPU utilization or increasing the scaling weight for specific hotspot service instances.

[0096] Specifically, simulated traffic is generated based on a pre-defined typical user behavior model. This simulated traffic is then used to conduct comprehensive stress tests and behavioral analysis on the combined e-commerce framework. This simulates various user access scenarios and load conditions that the e-commerce platform might face after launch, thereby obtaining performance data and user behavior patterns under different pressures. Secondly, based on the results of stress testing and behavioral analysis, potential performance bottlenecks and data access hotspots in the combined e-commerce framework under real-world operating conditions are predicted. Performance bottlenecks reveal where resource limitations might cause performance degradation, while data access hotspots indicate which data or services are frequently accessed, which are key factors affecting response speed. Furthermore, for the predicted performance bottlenecks, corresponding performance optimization configurations are injected into the e-commerce instance configuration. These configurations can include static caching rules to reduce dynamic calculations and database queries, database connection pool parameters to optimize database connection management, and distributed lock strategies to ensure data consistency under high concurrency. Simultaneously, for data access hotspots, corresponding data preheating instructions are injected into the e-commerce instance configuration to ensure that these frequently accessed hotspot data are pre-loaded into the cache when the e-commerce instance starts, thereby improving the response speed during the user's first access.

[0097] Through the above technical solution, this application can overcome the limitations of resource allocation based solely on static resource consumption characteristics. By introducing simulated traffic based on typical user behavior models for stress testing and behavior analysis, it can accurately predict the performance bottlenecks and data access hotspots of the e-commerce instance in a real operating environment. On this basis, targeted injection of performance optimization configurations and data preheating commands can effectively alleviate system bottlenecks and improve data access efficiency, thereby ensuring that the constructed e-commerce platform can continuously provide stable and efficient services when facing variable user traffic and business loads, improving user experience and reducing operating costs.

[0098] In one embodiment, step S50 includes: S51: Capture a snapshot of the runtime environment of the verified e-commerce preview instance to generate a system image including the complete runtime state; In this embodiment, snapshot capture refers to using containerization technology to create a reproducible read-only template, i.e., a system image, for the entire container (including operating system, runtime dependencies, application code, process state in memory, etc.) that hosts and runs the e-commerce preview instance. This system image not only contains static files, but also completely saves the environment state and context required for the application to run.

[0099] S52: Extract application code, verified configuration data, and corresponding static resource files related to the e-commerce business logic from the system image; In this embodiment, extraction refers to separating the parts directly related to the e-commerce business from the file system of the system image using image analysis tools or scripts; among them, application code refers to the source code or executable file that implements the e-commerce function; verified configuration data refers to the database connection string, API key and other running parameters confirmed by the user during the preview stage; static resource files refer to product images, front-end style sheets, script files, etc.

[0100] S53: Obtain the online operation requirements of the target object, perform dependency analysis and redundancy cleanup on the extracted application code, configuration data and static resource files, and perform standardized encapsulation based on the online operation requirements to generate encapsulated content; In this embodiment, online operation requirements refer to the specification constraints of the target object's production environment, such as CPU architecture, Java / Python version, security compliance requirements, etc.; dependency analysis and redundancy cleanup refer to using a package manager to analyze the dependency tree of the codebase and remove test dependencies, unused libraries, and debug files; standardized packaging refers to repackaging the cleaned assets into a format that conforms to the deployment specifications of the target environment according to online requirements, such as building a new, more streamlined Docker image, or packaging it as a HelmChart.

[0101] S54: Bind the packaged content to the service instance identifier corresponding to the e-commerce preview instance, and package it to generate a version-unique e-commerce deployment package that can be deployed independently.

[0102] In this embodiment, the service instance identifier refers to the unique ID assigned to the tenant when creating the preview instance; binding refers to writing the identifier as metadata (such as labels or annotations) into the encapsulated content (such as the labels of an image or the metadata of a chart); version uniqueness refers to automatically generating or associating a unique version number during packaging; the e-commerce deployment package refers to the final generated result, such as a tarball containing the business image and configuration, or a deployment description file containing all Kubernetes manifests.

[0103] Specifically, by capturing snapshots of the verified e-commerce preview instance's runtime environment, containerization technology is used to solidify the complete runtime state, including the operating system, runtime dependencies, application processes, and memory state, into an immutable system image, ensuring accurate reproduction of the environment from preview to deployment. Furthermore, application code directly related to the e-commerce business logic, configuration data confirmed by users during the preview phase, and static resource files such as product images and style sheets are extracted from the system image, effectively separating core business assets from the underlying runtime environment. Next, based on the predefined online runtime requirements of the target object, deep dependency analysis and redundancy cleanup are performed on the extracted application code, configuration data, and static resource files, removing development dependencies and useless files. These are then repackaged into a standardized production-ready format based on the specific specifications of the online environment, such as generating a streamlined container image or cloud-native application package. Finally, the packaged content is bound to the unique service instance identifier corresponding to the e-commerce preview instance and the automatically generated version number, packaging it into a unique e-commerce deployment package containing complete deployment metadata.

[0104] Through the above technical solutions, this application achieves a standardized conversion from preview instances to production deployment packages, improving consistency and reliability from verification environments to production environments. Specifically, dependency analysis and redundancy cleanup can reduce the size of deployment packages and potential security risks, and improve transmission and deployment efficiency. Standardized packaging based on online requirements can enhance the adaptability of e-commerce applications to different cloud infrastructures and improve portability. Finally, binding with service instance identifiers and unique versions provides an important technical foundation for SaaS platforms to achieve tenant application deployment, version rollback, canary releases, and full lifecycle management, effectively reducing operational complexity and costs.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] In one embodiment, a SaaS-based self-service e-commerce platform construction system is provided, which corresponds one-to-one with the SaaS-based self-service e-commerce platform construction method described in the above embodiments. The SaaS-based self-service e-commerce platform construction system includes: The feature extraction module is used to respond to the e-commerce platform construction request of the target object, obtain the corresponding demand information and parameter information, and determine the business form characteristics and interaction characteristics based on the demand information and parameter information. The component matching module is used to determine the corresponding basic framework template based on business characteristics, and to match several interactive functional components from the preset interactive component library based on interaction characteristics. The instance configuration generation module combines the matched basic framework template with interactive functional components, and generates the corresponding e-commerce instance configuration based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. The preview instance generation module is used to instantiate the e-commerce instance based on the e-commerce instance configuration, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification. The deployment package generation module is used to generate the corresponding e-commerce deployment package based on the verified e-commerce preview instance in response to the confirmation command of the target object. The e-commerce platform building module is used to publish the e-commerce platform deployment package to the online runtime environment allocated to the target audience, thereby completing the construction of the SaaS e-commerce platform.

[0107] Specific limitations regarding the construction system of a SaaS-based self-service e-commerce platform can be found in the limitations of the construction method for a SaaS-based self-service e-commerce platform described above, and will not be repeated here. Each module in the aforementioned SaaS-based self-service e-commerce platform construction system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0108] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for constructing a SaaS-based self-service e-commerce platform, characterized in that, Including the following steps: In response to the e-commerce platform building request from the target object, obtain the corresponding requirement information and parameter information, and determine the business form characteristics and interaction characteristics based on the requirement information and parameter information; Based on the characteristics of the business model, determine the corresponding basic framework template, and based on the interaction characteristics, match several interactive functional components from the preset interactive component library. The matched basic framework templates and interactive functional components are combined, and the corresponding e-commerce instance configuration is generated based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. Instantiate the instance based on the configuration of the e-commerce instance, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification; In response to the confirmation command from the target object, a corresponding e-commerce deployment package is generated based on the verified e-commerce preview instance; The e-commerce platform deployment package is published to the online operating environment allocated to the target audience, thus completing the construction of the SaaS e-commerce platform.

2. The method for constructing a SaaS-based self-service e-commerce platform according to claim 1, characterized in that: The steps of responding to the e-commerce platform construction request from the target object, obtaining the corresponding demand information and parameter information, and determining the business form characteristics and interaction characteristics based on the demand information and parameter information include: The requirements information is parsed and transformed into structured form information; Extract core business items from form information and map them to business form features according to a pre-set classification mapping table; Extract interface setting parameters and process setting parameters from parameter information, and determine interaction features based on preset judgment logic rules.

3. The method for constructing a SaaS-based self-operated e-commerce platform according to claim 1, characterized in that: The steps of determining the corresponding basic framework template based on business characteristics and matching several interactive functional components from a preset interactive component library based on interaction characteristics include: Input the business form characteristics into the pre-built template recommendation model, and the template recommendation model outputs template identifiers that match the business form characteristics; Based on business characteristics, all interactive functional components in the interactive component library are initially screened to generate a candidate interactive component set. The core interaction pattern is obtained by parsing the interaction features, and several suitable interaction function components are selected from the candidate interaction function component set according to the core interaction pattern. The core interaction pattern defines the main way for users to exchange data with the mall interface. Perform pre-validation of the interface and dependencies between the basic framework template corresponding to the template identifier and several selected interactive functional components to ensure that the interactive functional components can be integrated into the framework template.

4. The method for constructing a SaaS-based self-service e-commerce platform according to claim 3, characterized in that: The template recommendation model includes a feature parsing layer, an association matching layer, a decision optimization layer, and an identifier output layer. The step of inputting business form features into the pre-constructed template recommendation model and having the template recommendation model output template identifiers that match the business form features includes: The feature parsing layer performs a structured decomposition of business form features, generating a structured feature set that includes feature values ​​of multiple dimensions; The association matching layer calculates the matching degree between the structured feature set and the business dimension graph corresponding to each template in the pre-defined template library based on the predefined business logic graph, and generates an initial matching sequence. The decision optimization layer adjusts the weights of each matching item in the initial matching sequence based on historical deployment data, and filters out matching items below the preset success threshold to generate an optimized recommendation sequence. The identifier output layer determines template identifiers that are adapted to business model characteristics based on optimized recommendation sequences.

5. The method for constructing a SaaS-based self-operated e-commerce platform according to claim 4, characterized in that: The association matching layer, based on a predefined business logic graph, calculates the matching degree between the structured feature set and the business dimension graph corresponding to each template in the pre-set template library, and generates an initial matching order through the following steps: Extract several key business nodes and their relationships from the predefined business logic graph, and construct the target business logic subgraph corresponding to the structured feature set; The target business logic subgraph is aligned and compared with the standard business dimension graph associated with all templates in the template library, and the composite matching degree is calculated. The composite matching degree includes node coverage, relationship path consistency and business constraint satisfaction. The composite matching degree of each template is weighted and fused with its historical average deployment success rate to generate a comprehensive matching degree; All templates in the template library are sorted in descending order based on their overall matching degree to generate an initial matching sequence.

6. The method for constructing a SaaS-based self-operated e-commerce platform according to claim 1, characterized in that: The step of determining the corresponding basic framework template based on business characteristics and matching several interactive functional components from a preset interactive component library based on interaction characteristics further includes: When the interaction features fail to match the interaction function components, the reason for the failure is identified. The reasons for the failure include missing required functions, incompatible interfaces, and conflicts with the selected components. The interaction features that fail to match are mapped together with the business form features to a pre-built e-commerce knowledge graph. The graph reasoning is used to perform semantic enhancement and standardization on the interaction features that fail to match, and generate enhanced feature requirement descriptions. Based on the description of enhanced functional requirements, semantic retrieval and matching were performed again in the interactive component library to match alternative interactive functional components.

7. The method for constructing a SaaS-based self-operated e-commerce platform according to claim 1, characterized in that: The logical isolation unit includes a logical database instance, a file storage namespace, and a service instance identifier. The process involves combining the matched basic framework template with interactive functional components and generating a corresponding e-commerce instance configuration based on a predefined tenant isolation strategy. The predefined tenant isolation strategy includes the steps of creating an independent logical isolation unit for the target object and allocating configuration space and resource quotas, including: Dynamic dependency analysis is performed on the integration interface of the basic framework template and the service interface provided by the interactive functional components to identify and resolve potential call conflicts between the integration interface and the service interface, and generate a sequence and dependency relationship graph. Based on the sequence and dependency graph, interactive functional components are injected into the basic framework template, and version compatibility checks and runtime environment pre-verification are performed during the injection process to form a combined e-commerce framework. Based on the tenant isolation strategy, create independent logical database instances, file storage namespaces, and service instance identifiers for the combined e-commerce framework; Identify the resource consumption characteristics of interactive functional components, and allocate initial resource quotas and elastic scaling strategies based on these characteristics; The combined e-commerce framework, logical isolation units, and their associated initial resource quotas and elastic scaling strategies are encapsulated into an e-commerce instance configuration.

8. The method for constructing a SaaS-based self-service e-commerce platform according to claim 7, characterized in that: After the steps of identifying the resource consumption characteristics of the interactive functional components and allocating initial resource quotas and elastic scaling strategies based on the resource consumption characteristics, the method further includes: Simulated traffic is generated based on a pre-defined typical user behavior model, and stress testing and behavioral analysis are conducted on the combined e-commerce framework using the simulated traffic. Based on the results of stress testing and behavioral analysis, the performance bottlenecks and data access hotspots of the combined e-commerce framework in a real operating environment are predicted. Based on the performance bottleneck, inject corresponding performance optimization configurations into the mall instance configuration. The performance optimization configurations include one or more of the following: static caching rules, database connection pool parameters, and distributed lock strategies. Based on data access hotspots, corresponding data preheating instructions are injected into the mall instance configuration. These data preheating instructions are used to preload hotspot data into the cache when the mall instance starts.

9. The method for constructing a SaaS-based self-service e-commerce platform according to claim 1, characterized in that: The step of generating a corresponding e-commerce deployment package based on the verified e-commerce preview instance in response to the confirmation command of the target object includes: Capture a snapshot of the runtime environment of the verified e-commerce preview instance to generate a system image that includes the complete runtime state; Extract the application code, verified configuration data, and corresponding static resource files related to the e-commerce business logic from the system image; Obtain the online operation requirements of the target object, perform dependency analysis and redundancy cleanup on the extracted application code, configuration data and static resource files, and perform standardized encapsulation based on the online operation requirements to generate encapsulated content; Bind the packaged content to the service instance identifier corresponding to the e-commerce preview instance, and package it to generate an e-commerce deployment package that is version-unique and can be deployed independently.

10. A SaaS-based self-service e-commerce platform construction system, characterized in that, include: The feature extraction module is used to respond to the e-commerce platform construction request of the target object, obtain the corresponding demand information and parameter information, and determine the business form characteristics and interaction characteristics based on the demand information and parameter information. The component matching module is used to determine the corresponding basic framework template based on business characteristics, and to match several interactive functional components from the preset interactive component library based on interaction characteristics. The instance configuration generation module combines the matched basic framework template with interactive functional components, and generates the corresponding e-commerce instance configuration based on the predefined tenant isolation strategy. The predefined tenant isolation strategy includes creating independent logical isolation units for the target object and allocating configuration space and resource quotas. The preview instance generation module is used to instantiate the e-commerce instance based on the e-commerce instance configuration, generate an e-commerce preview instance, and provide the running status of the e-commerce preview instance to the target object for verification. The deployment package generation module is used to generate the corresponding e-commerce deployment package based on the verified e-commerce preview instance in response to the confirmation command of the target object. The e-commerce platform building module is used to publish the e-commerce platform deployment package to the online runtime environment allocated to the target audience, thereby completing the construction of the SaaS e-commerce platform.

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