Offline evaluation system for web front-end based enterprise eco-strategy

Abstract

The application discloses an offline evaluation system for enterprise ecological strategy based on a Web front end, comprising a front-end application layer, a resource management layer, a core function layer and a data storage layer; user registration is completed through the resource management layer, and a core shard storage module is pre-cached, and a WebAssembly inference engine module is initialized; user input is received through a user interface, and data input by the user is stored in the core shard storage module in real time, network state detection is performed through the resource management layer, when in an offline state, the WebAssembly inference engine module obtains data from the core shard storage module and performs relevant calculation tasks to determine the risk of the ecological system; the WebAssembly inference engine module transmits the calculation result to the front-end application end and visually presents it to the user. In the application, enterprise ecological strategy evaluation can be performed whether the user is in a network environment or not, the continuity of enterprise decision-making work is ensured, and internal data does not need to be uploaded to a third-party platform, so that the risk of leakage of core business secrets is reduced.

Description

Technical Field

[0001] This application relates to the field of enterprise management technology, and in particular to an offline evaluation system for enterprise ecosystem strategy based on a web front-end. Background Technology

[0002] In the field of modern corporate strategic management, companies need to regularly assess their strategic position and competitive advantages within the ecosystem. This is a crucial step for sustainable development and maintaining market competitiveness. Traditional corporate strategic assessments largely rely on cloud computing services to complete core analytical work. Data collection primarily involves using web crawling technology to obtain publicly available information such as company annual reports and industry reports. This collected, scattered data is then uploaded to a third-party server, which analyzes and models the data to ultimately output strategic analysis results for the company, helping it understand its position within the industry.

[0003] In existing technological solutions, the core computational process of the evaluation system relies entirely on cloud servers. Every step of data transmission and processing requires network support; without a network connection, the system cannot function properly. Users of such systems need to access the corresponding online platform through a browser, input relevant company information as required by the platform, and then wait for the cloud server to complete the analysis and return the results. Throughout this process, users are in a passive waiting state. Some existing systems provide users with basic visualization charts to display the company's scores in various evaluation dimensions, but these systems still do not escape their dependence on network connectivity. Once the network is interrupted, users cannot continue operating or viewing the results. Furthermore, the data collection of these systems mainly focuses on publicly available external data, lacking effective capture and integration of key information related to the company's internal operations, resulting in evaluation results that fail to fully reflect the company's true strategic situation. Summary of the Invention

[0004] The purpose of this application is to provide an offline evaluation system for enterprise ecosystem strategy based on a web front-end. This system enables enterprise ecosystem strategy evaluation regardless of whether the user is in a network environment, ensuring the continuity of enterprise decision-making. Furthermore, internal data does not need to be uploaded to a third-party platform, reducing the risk of leakage of core business secrets.

[0005] This application provides an offline evaluation system for enterprise ecosystem strategy based on Web front-end, including: front-end application layer, resource management layer, core function layer and data storage layer; the front-end application layer includes a user interface and interacts with the user through JavaScript logic, the data storage layer includes a core sharded storage module and a non-core sharded storage module, and the core function layer includes a WebAssembly inference engine module and a computing mode switching control module. During the system initialization phase: user registration is completed through the resource management layer and the core sharded storage module is pre-cached; the WebAssembly inference engine module is initialized. During the interactive evaluation phase: User input is received through the user interface and stored in the core shard storage module in real time. Network status is detected through the resource management layer. When the network is offline, the computing mode switching control module switches to offline mode, so that the WebAssembly inference engine module can obtain the user input data from the core shard storage module and perform relevant computing tasks to determine the ecosystem risk. During the results display phase: the WebAssembly inference engine module transmits the calculation results to the front-end application, and the front-end application layer presents them to the user through visual icons; The user-input data includes ratings based on five dimensions: ecosystem value proposition, ecosystem value acquisition, ecosystem market entry strategy, ecosystem openness, and ecosystem control intensity. The related computational tasks performed by the WebAssembly inference engine module include determining the risk level of each dimension based on the ratings and determining the ecosystem risk based on the levels.

[0006] In a preferred embodiment, for the ecosystem value claim, the scoring results include scoring the set questions and averaging the scoring results to calculate the corresponding score; For the acquisition of ecosystem value, the scoring results include the ranking percentage of net profit margin in the industry, and the corresponding score is calculated based on 1 - ranking percentage / 100; For the ecosystem market entry strategy, the scoring results include the year in which the ecosystem concept was first proposed by the company and the year in which the industry first proposed the ecosystem concept, and the difference between the two is calculated. The corresponding score is determined based on the difference. Regarding the openness of the ecosystem, the scoring results include scoring the set questions and averaging the scoring results to calculate the corresponding score; Regarding the intensity of ecosystem control, the scoring results include scoring of resource exchange, project cooperation, equity investment, and acquisition and control, and the corresponding score is calculated by weighted average of the scoring results.

[0007] In a preferred embodiment, when the score is between 0 and 0.2, the risk level of the corresponding dimension is low; when the score is between 0.2 and 0.8, the risk level of the corresponding dimension is medium; and when the score is between 0.8 and 1, the risk level of the corresponding dimension is high.

[0008] In a preferred embodiment, when the combination of the five dimensions includes four or more "low" values, the ecosystem risk is determined to be high risk; when the combination of the five dimensions is medium-medium-high-low-medium, the ecosystem risk is determined to be an innovation-closed ecosystem risk; when the combination of the five dimensions is low-low-medium-low-medium, the ecosystem risk is determined to be a low-profitability ecosystem risk; when the combination of the five dimensions is high-low-high-medium-low, the ecosystem risk is determined to be a market-advanced ecosystem risk; when the combination of the five dimensions is low-high-low-low-high, the ecosystem risk is determined to be a wild-growth ecosystem risk; and when the combination of the five dimensions does not include any "low" values, the ecosystem risk is determined to be low risk.

[0009] In a preferred embodiment, the front-end application layer includes a Canvas visualization engine for displaying the scores as radar charts and showing the ecosystem risk results.

[0010] In a preferred embodiment, when in an online state, the computing mode switching control module switches to online mode, causing the WebAssembly inference engine module to load industry benchmarking models from the non-core sharded storage module and perform industry benchmark comparison analysis, historical trend simulation, and future risk prediction.

[0011] In a preferred embodiment, the core functional layer further includes a threshold monitoring and early warning module, which monitors the scores of each dimension and triggers the front-end application layer to perform an early warning action when the score is lower than a safety threshold.

[0012] In a preferred embodiment, the warning action includes switching the interface display and red-marking the corresponding score.

[0013] In a preferred embodiment, the initialization of the WebAssembly inference engine module further includes: Complete the loading of binary instruction sets and adaptation to the runtime environment, and establish a browser-side computing channel; Complete the registration of local computing interfaces and configuration of call chains, and build a standardized computing service framework; Complete data storage association and permission verification, and establish a data read / write path with the local storage layer; Complete the evaluation model loading and parameter initialization to ensure that related computational tasks can be executed directly in offline mode.

[0014] In a preferred embodiment, the user-input data also includes data that is divided into eight core dimensions and reflects the actual operation of the enterprise, namely customer segments, value propositions, governance mechanisms, major complementarities, major competitors, key businesses, product launch timing, and value capture.

[0015] In a preferred embodiment, the customer segmentation is presented in the form of colored category cards in the front-end application layer; the value proposition is displayed in the form of core slogans plus role-specific bubbles in the front-end application layer; the key business is presented in the front-end application layer through section titles and business lists; the main complementarities are presented in the front-end application layer through type tags, enterprise nodes, and function connections; the governance mechanism is displayed in the front-end application layer through a hierarchical list; the main competitors are presented in the front-end application layer through business titles and competitor category lists; the product launch timing is displayed in the front-end application layer through product cards, timelines, and impact descriptions; and the value capture is presented in the front-end application layer through pie charts and data lists.

[0016] It should be understood that, within the scope of this invention, the above-described technical features of this invention and the technical features specifically described below (such as in the embodiments) can be combined with each other to form new or preferred technical solutions. Due to space limitations, they will not be described in detail here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. It should be understood that the accompanying drawings described below are merely some implementation examples of the present invention, and those skilled in the art can obtain other implementation examples based on these drawings without creative effort.

[0018] Figure 1 This is a block diagram of an offline evaluation system for an enterprise ecosystem strategy based on a web front-end, according to one embodiment of this application.

[0019] Figure 2 yes Figure 1 The diagram shows the process of evaluating the system.

[0020] Figure 3 This is a schematic diagram illustrating the offline evaluation process of an enterprise ecosystem strategy based on a web front-end, according to one embodiment of this application.

[0021] Figure 4 This is a schematic diagram of an ecosystem canvas tool module 1 according to one embodiment of this application.

[0022] Figure 5 This is a schematic diagram of Ecosystem Canvas Tool Module 2 according to one embodiment of this application.

[0023] Figure 6 This is a schematic diagram of the diagnostic results according to one embodiment of this application. Detailed Implementation

[0024] In the following description, many technical details are presented to help the reader better understand this application. However, those skilled in the art will understand that the technical solutions claimed in this application can be implemented even without these technical details and various variations and modifications based on the following embodiments.

[0025] Through extensive and in-depth research, the inventors have proposed an offline evaluation system and method for enterprise ecosystem strategies based on a web front-end. This invention integrates the resource management capabilities of Service Workers with the high-performance computing characteristics of WebAssembly to construct a visualized enterprise ecosystem strategy evaluation system that supports offline operation, overcoming the limitations of traditional online evaluation systems such as strong network dependence and data security risks. The system adopts a four-layer system architecture design, realizing the coordinated operation of front-end interactive resource scheduling, core computing, and data storage. An enterprise ecosystem canvas evaluation model is constructed, decomposing complex strategic evaluations into eight quantifiable core dimensions. An innovative layered and segmented loading mechanism ensures dynamic switching between basic functionality availability in offline environments and high-precision analysis in online environments. A localized inference engine and a visualization early warning module are developed to achieve local processing of evaluation data and intuitive risk presentation. In terms of functionality, an intuitive interactive interface guides users to complete self-scoring. The system stores input data in real time and initiates inference calculations, adaptively adjusting the calculation accuracy according to network conditions. Finally, the evaluation results are displayed in a visual format such as a radar chart. Simultaneously, risk warnings are triggered through interface color changes, providing secure, efficient, and network-unrestricted intelligent support for enterprise strategic decision-making.

[0026] This application has at least the following beneficial effects and advantages: 1. It enables offline availability of enterprise strategic assessment, completely eliminating the strong dependence of traditional systems on the network. Regardless of whether the user is in a network-free environment or a network-unstable scenario, the assessment operation and basic result analysis can be completed normally, ensuring the continuity of enterprise decision-making. It is especially suitable for senior executives who frequently travel on business and office scenarios in remote areas.

[0027] 2. The entire assessment data is stored and processed locally without being uploaded to a third-party server, which greatly reduces the risk of leakage of core business secrets and meets the strict data security requirements of enterprises. It is especially suitable for users with a large amount of sensitive data, such as large enterprises and government agencies.

[0028] 3. The layered and fragmented loading mechanism significantly improves system performance. Core fragment pre-caching reduces system startup time by more than 60%, allowing users to quickly enter the evaluation process. Non-core fragment dynamic loading balances inference accuracy and resource consumption, providing high-precision analysis in online environments and ensuring basic functions in offline environments, taking into account both user experience and practical needs.

[0029] 4. The visual early warning mechanism and autonomous interactive mode enhance the practicality and accuracy of the assessment. Users can independently input internal operational data, making the assessment results more in line with the actual situation of the enterprise. The intuitive radar chart display and gray warning interface help users quickly identify strategic risk points. The accompanying improvement suggestions provide clear guidance for enterprise strategic optimization, significantly improving the efficiency and quality of enterprise strategic management.

[0030] 5. The system has good scalability and adaptability. The layered architecture design supports adding new evaluation dimensions, optimizing algorithm models, or expanding industry templates without modifying the overall architecture. It can be flexibly adjusted according to the needs of different industries and enterprise sizes, has a wide range of applications, and has significant commercial value and promotion potential.

[0031] Explanation of some concepts: Offline inference refers to the process by which a system independently completes inference calculations and analysis without relying on cloud servers or network data transmission, based on its core algorithm components and data resources stored locally. This ensures that basic services remain available even in network-free scenarios.

[0032] User-driven interaction involves users inputting information for various evaluation dimensions, subjective ratings, and demand selections on the system's interface based on the company's actual operations. The operating system responds in real time and provides feedback on the results, forming a complete user-led interactive loop.

[0033] The Enterprise Ecosystem Canvas Assessment Model is an analytical framework built on the theory of enterprise ecosystems. It breaks down enterprise strategic assessment into eight core dimensions: customer segmentation, value proposition, governance mechanism, major complementarities, major competitors, key businesses, product launch timing, and value capture. These dimensions are interconnected to form a complete strategic analysis system.

[0034] Service Worker is a script that runs in the background of the browser and has capabilities such as network request interception, resource cache management, and offline status monitoring. As the core scheduling hub of the system, it is responsible for coordinating the dynamic use of local and network resources.

[0035] WebAssembly is a high-performance binary instruction format that can run complex algorithms in a browser environment with near-native code execution efficiency, providing efficient local inference computing support for the system.

[0036] IndexedDB is a browser-built-in local database with persistent storage capabilities. It can securely store large amounts of structured data, and core data will not be lost even if the user clears the browser cache, providing data support for offline inference.

[0037] Visualized early warning is a system that transforms assessment results into intuitive graphical displays and provides risk alerts for assessment dimensions that fall below the safety threshold through color changes, interface style adjustments, and other means, helping users quickly identify strategic risk points.

[0038] Layered and sharded loading breaks down complex AI inference models into core shards and non-core shards. Core shards contain basic algorithms and key data and are forcibly cached in IndexedDB to ensure offline availability. Non-core shards contain extended functions and high-precision algorithms and are dynamically loaded based on network conditions to achieve a balance between performance and resource consumption.

[0039] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0040] This application discloses an offline evaluation system for enterprise ecosystem strategy based on a web front-end, the block diagram of which is shown below. Figure 1 As shown, the evaluation system comprises a front-end application layer, a resource management layer, a core functionality layer, and a data storage layer. The front-end application layer includes a user interface that interacts with the user through JavaScript logic. The data storage layer includes a core sharded storage module and a non-core sharded storage module. The core functionality layer includes a WebAssembly inference engine module and a computation mode switching control module.

[0041] Figure 2 and Figure 3 It shows Figure 1 The diagram illustrates the system evaluation process. During system initialization: User registration is completed through the resource management layer, and the core sharded storage module is pre-cached. The WebAssembly inference engine module is initialized. During interactive evaluation: User input is received through the user interface and stored in the core sharded storage module in real time. Network status is monitored through the resource management layer. When offline, the computation mode switching control module switches to offline mode, allowing the WebAssembly inference engine module to retrieve user input data from the core sharded storage module and execute relevant computation tasks to determine ecosystem risk. During results display: The WebAssembly inference engine module transmits the computation results to the front-end application, which presents them to the user through visual icons. The user input data includes scores given by the user on five dimensions: ecosystem value proposition, ecosystem value capture, ecosystem market entry strategy, ecosystem openness, and ecosystem control strength. The computation tasks performed by the WebAssembly inference engine module include determining the risk level of each dimension based on the scores and determining the ecosystem risk based on the levels.

[0042] To better understand the technical solution of this application, a specific example is provided below. The details listed in this example are mainly for ease of understanding and are not intended to limit the scope of protection of this application.

[0043] Existing technologies have significant limitations in data collection, relying solely on web crawlers to obtain publicly available data. This makes it difficult to reflect key strategic elements such as internal governance efficiency and the closeness of partnerships, resulting in a lack of comprehensiveness and authenticity in the assessment results. In-depth industry data is often limited by API interface fees or access restrictions, making it difficult for SMEs to obtain high-quality benchmarking data, thus affecting the reference value of the assessment.

[0044] Traditional systems rely entirely on network connectivity. In environments with unstable or no network access, such as underground meeting rooms during business trips, services will fail, compromising the continuity of corporate decision-making. The full loading of complex models results in excessively long initial system startup times, leading to a poor user experience. Furthermore, the need to upload evaluation data to third-party servers poses a security risk of leaking core business secrets.

[0045] This invention guides users to input real internal operational data through a user-initiated interactive mode, compensating for the deficiencies of externally crawled data and thus improving the relevance and accuracy of the evaluation results. Employing layered and segmented loading and offline inference technologies, core functional modules are locally cached, completely resolving service failures in offline environments and ensuring that decision-making is not limited by network connectivity. By storing evaluation data locally and performing inference calculations, sensitive data is avoided from being uploaded to third-party servers, significantly reducing the risk of trade secret leaks. The optimized model loading mechanism pre-caches core segments to reduce startup time, while dynamically loads non-core segments to balance performance and resource consumption. Furthermore, an innovative visual early warning function enables enterprises to quickly identify strategic risk points, comprehensively improving the practicality and security of the evaluation system.

[0046] This system is an ecosystem management aid for enterprises. Its core functions include two major modules: ecosystem canvas drawing and ecosystem self-diagnosis. Through ecological visualization and multi-dimensional risk diagnosis, it helps enterprises clearly understand and scientifically assess the status of their own ecosystem.

[0047] Before using the system, users need to enter their company name. After clicking "OK," a welcome message will appear on the page, displaying the company name for one second before automatically entering the function selection interface. The function selection interface clearly presents two core functions and notes that all data is processed only in the current session and is not stored on the server, ensuring user data security.

[0048] Module 1: Drawing the Ecosystem Canvas 1. Functional Objectives By collecting core information about an enterprise's ecosystem through structured tabular input, a visual ecosystem canvas is automatically generated, helping enterprises intuitively present the eight core dimensions of their ecosystem layout, clearly identify key elements such as customers, value, business, and partners, and provide a clear reference for enterprise ecosystem strategic planning.

[0049] 2. Operating Procedures Upon entering this module, the system will display eight tabular input interfaces, each corresponding to a core module of the canvas. Businesses must accurately fill in the specific information according to the table columns. Each module allows users to add rows themselves, meeting the diverse information entry needs of businesses. Figure 4 The diagram shows the interface of Module 1 of the Ecosystem Canvas. The core columns of the Customer Segmentation module include customer segment dimensions, core characteristics, target products, and price range. The Value Proposition module covers core customer value, ecosystem profit logic, and ecosystem roles. The Key Business module includes business segments and specific business content. The Major Complementaries module involves complementarity types, core partners, and their core roles in the ecosystem. The Governance Mechanism module lists governance methods and specific measures. The Major Competitors module includes core businesses, domestic competitors, foreign competitors, and competitive areas. The Product Launch Timing module covers core products, their own launch time, competitors' launch time, and market impact. The Value Capture module includes revenue sources, revenue share, core cost items, year-on-year cost changes, net profit, and net profit margin. Table 1 shows the eight sub-modules of the Ecosystem Canvas. Table 1 After completing all forms, users click the "Generate Visual Canvas" button. The system automatically extracts the data from the forms and generates a complete visual ecosystem canvas according to preset rules. In the generated canvas, customer segments are presented as color-coded category cards. Value propositions are displayed using core slogans and role-specific bubbles. Key business segments are presented with section titles and business lists. Major complementarities are presented with type tags, company nodes, and function connections. Governance mechanisms are displayed as hierarchical lists. Major competitors are presented with business titles and competitor category lists. Product launch timing is displayed with product cards, timelines, and impact descriptions. Value capture is presented with pie charts and data lists.

[0050] The generated visualization canvas supports online zooming and viewing, and also provides export functions in image and PDF formats, making it convenient for enterprises to archive or share, and meeting the usage needs of enterprises in different scenarios.

[0051] Module Two: Ecosystem Self-Diagnosis 1. Functional Objectives From a lifecycle perspective, a business ecosystem needs to make a series of important decisions at different stages of development. These decisions not only directly affect the financial health and market competitiveness of companies within the system, thus determining their survival prospects, but also, in the long run, have a crucial impact on the direction of the entire business ecosystem.

[0052] By reviewing core research in the field of business ecosystems, this application summarizes five key decision categories that companies within an ecosystem need to make, and defines them as core variables that influence the development of a business ecosystem, including value proposition, value capture, market entry strategy, openness, and control, as shown in Table 2 below. Table 2 By collecting relevant data from enterprises through quantitative questions across five dimensions—ecosystem value proposition, ecosystem value capture, ecosystem market entry strategy, ecosystem openness, and ecosystem control intensity—the system automatically calculates scores for each dimension and generates radar charts. Simultaneously, it determines the type of ecosystem risk based on the combination of dimension levels, helping enterprises quickly identify potential risks or confirm the health status of their ecosystems and providing direction for ecosystem optimization. Figure 5 The diagram shows the display interface of Ecosystem Canvas Module 2.

[0053] 2. Diagnostic Process Step 1: Fill in the 5-dimensional questions Companies need to complete the questions in five dimensions. The system will automatically calculate the score for each dimension, and the score for all dimensions is between 0 and 1.

[0054] Dimension One is the ecosystem value proposition. The core of this dimension involves companies scoring themselves on three related questions, each scored from 0 to 100, where 0 represents no compliance and 100 represents full compliance. These three questions are: the ecosystem value proposition is specific and effectively applicable to all participants within the ecosystem; the ecosystem value proposition aligns with customer needs; and the company's resources and capabilities are sufficient to realize the proposed value proposition. The scoring logic for this dimension is the average score of the three questions divided by 100, as shown in Formula 1 below.

[0055] (Formula 1) Dimension Two is the acquisition of ecosystem value. Enterprises need to fill in the ranking of their net profit margin in their industry. The input content is an integer between 0 and 100. The calculation logic is 1 minus the input value and divided by 100. The calculation formula is shown in Formula 2 below.

[0056] (Formula 2) Dimension 3 is the ecosystem market entry strategy. Companies need to fill in two years: the year they first proposed the ecosystem concept and the year when a Chinese company in their industry first proposed the ecosystem concept, both in four-digit numbers. The calculation logic is to first calculate the difference between the two years, subtract the result of the next question from the previous one, and then assign a score according to the corresponding rules based on the difference. The calculation method is shown in Table 3 below. Table 3 Dimension four is ecosystem openness. Companies need to score two questions on a scale of 0 to 100, where 0 represents extremely low openness and 100 represents extremely high openness. These two questions are: a large number of ecosystem participants, including customers, complementarities, and partners; and a high degree of diversity among ecosystem participants, encompassing diversity in industry, size, and role. The score for this dimension is calculated by averaging the scores from the two questions and dividing by 100, as shown in Formula 3 below.

[0057] (Formula 3) Dimension 5 is the intensity of ecosystem control. Enterprises need to assign weighted scores to four control methods: resource exchange, project cooperation, equity investment, and acquisition / controlling stake. The base scores for these four methods are fixed at 20, 50, 80, and 100 points respectively. Enterprises must allocate a weight percentage to each control method, and the sum of the four weight percentages must be 100%; otherwise, submission will fail. The scoring logic for this dimension is: multiply the base score of each control method by the sum of its corresponding weights, then divide by 100. The calculation method is shown in Table 4 below. Table 4 Step 2: Review the diagnostic results After completing the questionnaire, users can view the diagnostic results. The system generates two core outputs based on the scores across five dimensions. First, a radar chart visualization visually presents the score distribution across the five dimensions, allowing companies to clearly understand the performance differences in each dimension. Second, a risk diagnosis. The system first maps each dimension score to a corresponding level: 0 to 0.2 is low, 0.2 to 0.8 is medium, and 0.8 to 1 is high. Then, it matches corresponding prompts based on the level combinations. If four or more dimensions are at low levels, the ecosystem risk is considered too high. If there are one to three low levels, different level combinations correspond to different risk types, as shown in Table 5. For example, a level combination of medium-medium-high-low-medium indicates a risk of an innovative but closed ecosystem. A level combination of low-low-medium-low-medium may indicate a risk of a weak-profit ecosystem; a level combination of high-low-high-medium-low may indicate a risk of a market-advanced ecosystem; and a level combination of low-high-low-low-high may indicate a risk of a rapidly growing ecosystem. Other combinations of one to three low levels are considered to indicate no significant risk at present, but improvement is needed. If there are 0 low-level ratings, the ecosystem is considered healthy. The system also provides detailed analysis and improvement suggestions for each dimension, generating a complete assessment report that users can export as a PDF for future reference. This provides specific guidance for enterprise ecosystem optimization, such as... Figure 6 As shown. Table 5 The system of this invention adopts a four-layer architecture design (such as...). Figure 1 As shown in the diagram, from top to bottom, the layers are the front-end application layer, the Service Worker resource management layer, the core function layer, and the data storage layer. Each layer has clearly defined responsibilities and works collaboratively to ensure the overall stability of the system's functions.

[0058] The front-end application layer is developed using modern Web technologies and employs a responsive layout to flexibly adapt to both PC and mobile devices. It primarily handles core tasks such as user interface presentation, data input evaluation, visualization of results, and triggering of alert mechanisms. This layer utilizes native JavaScript to build smooth interactive logic and leverages Canvas technology to draw dynamic radar charts and other visualizations. This ensures a smooth and convenient user experience while making results more intuitive and easy to understand, satisfying users' dual needs for interactive experience and information presentation.

[0059] As the core scheduling hub of the system, the Service Worker resource management layer shoulders key responsibilities such as network status detection, request interception, resource caching, and loading scheduling. During system initialization, the Service Worker automatically completes the registration process and pre-caches core shards. These core shards cover key resources such as basic algorithms, weight data, and interface templates for eight evaluation dimensions, and are persistently stored in IndexedDB, laying the foundation for subsequent offline use. When a user initiates an evaluation request, the Service Worker monitors the current network status in real time. If offline, it directly calls the locally cached core shards to ensure the normal operation of basic evaluation functions. If online, it asynchronously loads non-core shards, which include extended resources such as industry benchmarking models and high-precision analysis algorithms. These resources are stored in CacheAPI and will then be dynamically linked to the core functional layer to support high-precision analysis.

[0060] The core functional layer comprises the WebAssembly inference engine module, the online / offline switching control module, and the visualization and early warning module, forming the core of the system's efficient computation and intelligent early warning capabilities. The WebAssembly inference engine, as the computational core, has compiled binary code that runs efficiently in a browser and can flexibly adjust computational strategies based on loaded shard resources. In offline mode, it utilizes core shards to perform basic weight calculations and score statistics, meeting users' basic assessment needs. In online mode, it integrates non-core shards to perform in-depth analysis, including advanced functions such as industry benchmark comparison, historical trend simulation, and future risk prediction, providing users with a more comprehensive assessment reference. Specifically, the inference engine dynamically merges and calls industry benchmark data and standardized analysis algorithms loaded in non-core shards with local computational models in core shards. It performs horizontal comparative analysis between local assessment scores and industry benchmark data, conducts time-series trend simulations based on historical assessment records, and uses fitting algorithms to predict future risk probabilities by combining multi-dimensional assessment indicators. The online / offline switching control module receives network status signals transmitted by the Service Worker in real time and automatically switches the inference engine's computational mode. The entire switching process is completely seamless for the user, ensuring the continuity of user operations. The visualization and early warning module continuously monitors the scores across various dimensions. When a score is detected to be below the safety threshold, it immediately triggers warning actions such as switching the interface style and highlighting red indicators. At the same time, it calls the preset risk analysis database to generate targeted improvement suggestions, helping users to promptly identify and address potential risks.

[0061] The data storage layer employs a dual storage strategy combining IndexedDB and CacheAPI, balancing data persistence and access efficiency. IndexedDB primarily stores critical data such as core shards, user-inputted evaluation data, basic enterprise information, and version metadata, ensuring persistent data preservation. Even if the user clears the browser cache, core data will not be lost, providing reliable data support for offline inference. CacheAPI focuses on storing non-core shards, static UI resources, and temporary analysis results. Through fast caching and retrieval, it effectively improves resource access speed and optimizes the user experience. Furthermore, the system implements a versioned incremental update mechanism. When the evaluation model or algorithm needs iterative upgrades, there's no need to reload the complete model; only the difference shards need to be downloaded and merged with the local cache. This significantly reduces traffic consumption and iteration costs during the update process, improving system iteration efficiency.

[0062] The core process of the system is mainly divided into four key stages: system initialization, user interaction evaluation, inference computation, and result display. In the system initialization stage, the Service Worker completes registration and pre-caches core shards, while the WebAssembly engine completes four standardized initialization steps to prepare for subsequent operations: 1) Loading the binary instruction set and adapting the runtime environment to establish an efficient computation channel on the browser side; 2) Registering the local computation interface and configuring the call chain to build a standardized computation service framework; 3) Associating data storage and verifying permissions to establish a data read / write path with the local storage layer; 4) Loading the basic evaluation model and initializing parameters to ensure that basic computation can be started directly in offline mode. In the user interaction evaluation stage, the front-end application layer receives various evaluation data input by the user and stores this data in IndexedDB in real time to ensure no data loss. In the inference computation stage, the Service Worker monitors the network status in real time and calls the corresponding shard resources according to different network statuses, starting the WebAssembly engine to execute relevant computation tasks (such as the computations performed in the aforementioned diagnostic process). In the results presentation stage, the core functional layer transmits the calculated results to the front-end application layer and presents them to the user in the form of visual charts. If the score is lower than the safety threshold, an early warning mechanism will be triggered simultaneously. Finally, a complete evaluation report is generated, which can be exported and saved by the user, providing a comprehensive and reliable reference for enterprise strategic decision-making.

[0063] Accordingly, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the various method embodiments of this application. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media do not include transient computer-readable media, such as modulated data signals and carrier waves.

[0064] Furthermore, embodiments of this application also provide an offline evaluation system for enterprise ecosystem strategies based on a web front-end, including a memory for storing computer-executable instructions and a processor; the processor is used to implement the steps in the above-described method embodiments when executing the computer-executable instructions in the memory. The processor may be a Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processor (DSP), Microcontroller Unit (MCU), Neural Processing Unit (NPU), Application Specific Integrated Circuit (ASIC), Field Programmable Gate Array (FPGA), or other programmable logic devices. The aforementioned memory may be read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or solid-state drive, etc. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor.

[0065] Furthermore, embodiments of this application also provide a computer program product, including computer-executable instructions that, when executed by a processor, implement the steps in the above-described method embodiments.

[0066] All references to this specification are considered to be incorporated integrally into the disclosure of this application so that they can serve as the basis for modifications if necessary. Furthermore, it should be understood that the above descriptions are merely preferred embodiments of this specification and are not intended to limit the scope of protection of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the scope of protection of one or more embodiments of this specification.

[0067] In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An offline evaluation system for enterprise ecosystem strategy based on a web front-end, characterized in that, include: Front-end application layer, resource management layer, core function layer, and data storage layer; The front-end application layer includes a user interface and interacts with the user through JavaScript logic; the data storage layer includes a core sharded storage module and a non-core sharded storage module; and the core functional layer includes a WebAssembly inference engine module and a computing mode switching control module. During the system initialization phase: user registration is completed through the resource management layer and the core sharded storage module is pre-cached; the WebAssembly inference engine module is initialized. During the interactive evaluation phase: User input is received through the user interface and stored in the core shard storage module in real time. Network status is detected through the resource management layer. When the network is offline, the computing mode switching control module switches to offline mode, so that the WebAssembly inference engine module can obtain the user input data from the core shard storage module and perform relevant computing tasks to determine the ecosystem risk. During the results display phase: the WebAssembly inference engine module transmits the calculation results to the front-end application, and the front-end application layer presents them to the user through visual icons; The user-input data includes ratings based on five dimensions: ecosystem value proposition, ecosystem value acquisition, ecosystem market entry strategy, ecosystem openness, and ecosystem control intensity. The related computational tasks performed by the WebAssembly inference engine module include determining the risk level of each dimension based on the ratings and determining the ecosystem risk based on the levels.

2. The system as described in claim 1, characterized in that: For the ecosystem value claim, the scoring results include scoring the set questions and averaging the scoring results to calculate the corresponding score; For the acquisition of ecosystem value, the scoring results include the ranking percentage of net profit margin in the industry, and the corresponding score is calculated based on 1 - ranking percentage / 100; For the ecosystem market entry strategy, the scoring results include the year in which the ecosystem concept was first proposed by the company and the year in which the industry first proposed the ecosystem concept, and the difference between the two is calculated. The corresponding score is determined based on the difference. Regarding the openness of the ecosystem, the scoring results include scoring the set questions and averaging the scoring results to calculate the corresponding score; Regarding the intensity of ecosystem control, the scoring results include scoring of resource exchange, project cooperation, equity investment, and acquisition and control, and the corresponding score is calculated by weighted average of the scoring results.

3. The system as described in claim 2, characterized in that, When the score is between 0 and 0.2, the risk level of the corresponding dimension is low; when the score is between 0.2 and 0.8, the risk level of the corresponding dimension is medium; and when the score is between 0.8 and 1, the risk level of the corresponding dimension is high.

4. The system as described in claim 3, characterized in that, When the combination of the five dimensions includes four or more "low" values, the ecosystem risk is determined to be high risk; when the combination of the five dimensions is medium-medium-high-low-medium, the ecosystem risk is determined to be an innovation-closed ecosystem risk; when the combination of the five dimensions is low-low-medium-low-medium, the ecosystem risk is determined to be a low-profitability ecosystem risk; when the combination of the five dimensions is high-low-high-medium-low, the ecosystem risk is determined to be a market-advanced ecosystem risk; when the combination of the five dimensions is low-high-low-low-high, the ecosystem risk is determined to be a wild growth ecosystem risk; when the combination of the five dimensions does not include any "low" values, the ecosystem risk is determined to be low risk.

5. The system as described in claim 4, characterized in that, The front-end application layer includes a Canvas visualization engine for displaying the scores as radar charts and showing the ecosystem risk results.

6. The system as described in claim 1, characterized in that, When in an online state, the computing mode switching control module switches to online mode, enabling the WebAssembly inference engine module to load industry benchmarking models from the non-core sharded storage module and perform industry benchmark comparison analysis, historical trend simulation, and future risk prediction.

7. The system as described in claim 1, characterized in that, The core functional layer also includes a threshold monitoring and early warning module, which monitors the scores of each dimension and triggers the front-end application layer to perform an early warning action when the score is lower than the safety threshold.

8. The system as described in claim 1, characterized in that, The initialization of the WebAssembly inference engine module further includes: Complete the loading of binary instruction sets and adaptation to the runtime environment, and establish a browser-side computing channel; Complete the registration of local computing interfaces and configuration of call chains, and build a standardized computing service framework; Complete data storage association and permission verification, and establish a data read / write path with the local storage layer; Complete the evaluation model loading and parameter initialization to ensure that related computational tasks can be executed directly in offline mode.

9. The system as described in claim 1, characterized in that, The user-input data also includes data that is divided into eight core dimensions, namely customer segments, value proposition, governance mechanisms, major complementarities, major competitors, key businesses, product launch timing, and value capture, and reflects the actual operation of the enterprise.

10. The system as described in claim 9, characterized in that, The customer segments are presented in the form of colored category cards in the front-end application layer. The value proposition is displayed in the form of core slogans plus role bubbles in the front-end application layer. The key businesses are presented in the front-end application layer through section titles and business lists. The main complementarities are presented in the front-end application layer through type tags, enterprise nodes, and function connections. The governance mechanism is displayed in the front-end application layer through a hierarchical list. The main competitors are presented in the front-end application layer through business titles and competitor category lists. The product launch timing is displayed in the front-end application layer through product cards, timelines, and impact descriptions. The value capture is presented in the front-end application layer through pie charts and data lists.

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