H5 offline and resource increment updating system of designated driver App

Abstract

The invention relates to the technical field of mobile application development, in particular to an H5 offline and resource increment updating system of a designated driver App, which comprises a data acquisition module for acquiring designated driver App core H5 page resource data, interface configuration information and designated driver scene feature data; the incremental updating module constructs a local resource version list, performs differentiation verification, and triggers an incremental updating process if version difference exists; the off-line cache management module constructs an off-line cache management system, performs hierarchical caching on core H5 resources, generates an off-line available resource pool and establishes a cache dynamic elimination mechanism; a network adaptive scheduling module triggers a designated driving core function reservation and non-core function degradation mechanism according to a network state adaptive scheduling strategy; and the data synchronization module synchronizes the designated driver operation data in the offline state to the server, and updates the local resource version list and the cache state. Therefore, the problems of low updating efficiency, lack of degradation capability, extremely poor offline availability and the like in the prior art are solved.

Description

Technical Field

[0001] This invention relates to the field of mobile application development technology, specifically to an H5 offline version and incremental resource update system for a ride-hailing app. Background Technology

[0002] In the development of Hybrid Apps such as ride-hailing services, various existing technologies have been introduced and applied to optimize H5 page loading performance and offline experience. Among these, browser standard caching mechanisms, by setting cache header information, allow browsers to store static H5 resources locally. Upon subsequent accesses within the validity period, these resources can be loaded directly from the local storage, thereby reducing network requests and improving page loading speed. In addition, some solutions also employ simple local storage technologies, such as WebStorage or the app's own storage space, to pre-install or cache some H5 resources on the user's device, such as... Figure 1 As shown. In recent years, the emergence of ServiceWorker technology has marked a significant advancement. As a programmable network proxy, it can intercept and process network requests, providing a strong technical foundation for implementing smarter and more refined resource caching strategies, and bringing new possibilities for improving the user experience of H5 in weak network environments.

[0003] The existing technical solution has significant flaws, severely restricting user experience and business continuity. Regarding the update mechanism, it adopts a complete resource package update mode. Even if only a single file changes, users need to re-download all content. This not only leads to low update efficiency and long processing times but also causes huge bandwidth consumption. Furthermore, due to the lack of breakpoint resumption capabilities, network interruptions result in complete loss of all data. In terms of caching and offline strategies, the solution is extremely fragile and lacks degradation capabilities. Cached content is strongly bound to specific versions; once a user hasn't loaded it or the cache is cleared, the H5 functionality becomes completely unavailable in offline environments, resulting in extremely poor offline availability. Users are unable to perform core operations such as order placement, directly causing order loss. In terms of management granularity, coarse-grained version management cannot identify changes to individual files, leading to a large number of unchanged resources being downloaded repeatedly. The lack of differentiated resource management further exacerbates bandwidth waste and efficiency problems. Summary of the Invention

[0004] This application provides an H5 offline and incremental resource update system for a ride-hailing app to solve problems such as low update efficiency, lack of degradation capability, and extremely poor offline usability in the prior art.

[0005] The first aspect of this application provides an offline H5 update and incremental resource update system for a ride-hailing app, comprising: a data acquisition module, an incremental update module, an offline cache management module, a network adaptive scheduling module, and a data synchronization module. The data acquisition module collects core H5 page resource data, interface configuration information, and ride-hailing scenario characteristic data from the ride-hailing app. The incremental update module constructs a local resource version list and performs differential verification with the server-side resource version list. If version differences exist, an incremental update process is triggered, using a breakpoint resume engine to download and verify the different resources in blocks. The offline cache management module constructs an offline cache management system using ServiceWorker, caching core H5 resources hierarchically according to ride-hailing scenario priority, generating an offline available resource pool, and establishing a dynamic cache eviction mechanism. The network adaptive scheduling module uses an adaptive scheduling strategy based on network status, synchronizing incremental updates and activating full H5 functionality when online, and enabling the offline available resource pool when offline, triggering a mechanism to retain core ride-hailing functions and degrade non-core functions. The data synchronization module synchronizes ride-hailing operation data from the offline state to the server using a data consistency verification algorithm after network recovery, while simultaneously updating the local resource version list and cache status.

[0006] Preferably, the data acquisition module includes a resource list acquisition unit, an interface configuration acquisition unit, and a scene feature acquisition unit. The resource list acquisition unit periodically obtains a version list of H5 static resources from the server and records the hash value version number of each file. The interface configuration acquisition unit specifically collects the configuration information of API interfaces related to the chauffeur service. The scene feature acquisition unit collects specific environmental data of the chauffeur service in real time and dynamically identifies business priorities.

[0007] Preferably, the incremental update module includes a list acquisition unit, a differential comparison unit, an incremental download unit, and a list synchronization unit. The list acquisition unit actively requests a resource version list from the server and records the hash value version number of each H5 static file. The differential comparison unit accurately identifies newly added, modified, or deleted files by comparing the hash values ​​of the online and local lists item by item, performing file-level change detection. The incremental download unit, based on the differential list, uses a breakpoint resume engine to download only changed resources, supporting recovery from the point of interruption after network interruption. The list synchronization unit atomically updates the local list after downloading.

[0008] Preferably, the offline cache management module includes a ServiceWorker management unit, a resource hierarchical caching unit, an offline resource pool maintenance unit, and a cache dynamic eviction unit. The ServiceWorker management unit is responsible for registering and activating ServiceWorkers, intercepting resource requests from H5 pages and executing cache priority strategies, and managing the caching logic for multiple versions of resources. The resource hierarchical caching unit categorizes and caches H5 static resources according to the priority of the ride-hailing scenario. The offline resource pool maintenance unit integrates the effective resources after hierarchical caching, constructs a structured offline available resource pool, and updates the resource status in real time. The cache dynamic eviction unit automatically evicts low-priority resources that have not been used for a long time based on resource usage frequency, cache validity period, and device storage capacity.

[0009] Preferably, the network adaptive scheduling module includes a network status detection unit, a strategy decision-making unit, and a function scheduling unit. The network status detection unit is responsible for real-time monitoring of the device's network connection status, using a heartbeat mechanism or API response timeout judgment to provide basic data input for adaptive scheduling. The strategy decision-making unit dynamically selects the optimal strategy based on the network status, prioritizing the incremental update of resource synchronization and activating the full H5 functionality when online, and automatically switching to a degraded mode when offline. The function scheduling unit specifically executes the strategy decisions, coordinating resource downloads and function activation when online, and activating the offline available resource pool when offline, prioritizing the retention of the core chauffeur service function and degrading non-core functions.

[0010] Preferably, the data synchronization module includes an offline data collection unit, a data verification and synchronization unit, and a status update unit. The offline data collection unit is responsible for continuously collecting ride-hailing operation data during offline periods and temporarily storing it in a local queue. After the network is restored, the data verification and synchronization unit verifies the integrity of the offline data through a data consistency verification algorithm and then securely transmits it to the server. After successful synchronization, the status update unit atomically updates the local resource version list and cache status, synchronizes server-side changes, and cleans up processed data.

[0011] The second aspect of this application provides a method for an offline H5 update and incremental resource update system for a ride-hailing app, comprising: collecting core H5 page resource data, interface configuration information, and ride-hailing scenario feature data of the ride-hailing app; constructing a local resource version list based on the core H5 page resource data, interface configuration information, and ride-hailing scenario feature data, performing differential verification with the server-side resource version list, and triggering an incremental update process if version differences exist, using a breakpoint resume download engine to download and verify the different resources in blocks, and simultaneously constructing an offline cache management system based on ServiceWorker, caching core H5 resources hierarchically according to ride-hailing scenario priority, generating an offline available resource pool, and establishing a cache dynamic eviction mechanism; according to the cache dynamic eviction mechanism, using a network state adaptive scheduling strategy, synchronously updating resources and activating the complete H5 function when online, and enabling the offline available resource pool when offline, triggering a mechanism for retaining core ride-hailing functions and downgrading non-core functions, and when the network is restored, synchronizing the ride-hailing operation data in the offline state to the server through a data consistency verification algorithm, and simultaneously updating the local resource version list and cache state.

[0012] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement a method for an H5 offline version and incremental resource update system for a ride-hailing app as described in the above embodiments.

[0013] A fourth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement a method for an H5 offline system and incremental resource update system for a ride-hailing app as described in the above embodiments.

[0014] The fifth aspect of this application provides a computer program product, including a computer program or instructions, for implementing a method for an H5 offline version and incremental resource update system for a ride-hailing app as described in the above embodiments.

[0015] Therefore, this application has the following beneficial effects: This application's embodiments achieve efficient and stable H5 offline processing and resource management capabilities through multi-module collaboration. The data acquisition module provides a precise data foundation for resource version management and scenario adaptation, ensuring the targeted nature of subsequent operations. The incremental update module significantly reduces resource update traffic consumption and time costs, improving update efficiency through version list comparison and breakpoint resume download in chunks. The offline cache management module relies on ServiceWorker hierarchical caching of core resources and dynamically eliminates redundant content, building a highly available offline resource pool to ensure the stable operation of core ride-hailing functions in weak or no-network scenarios. The network adaptive scheduling module intelligently switches strategies based on real-time network status, promptly synchronizing and activating full functionality when online, and triggering function degradation when offline to ensure basic service availability, significantly enhancing application environment adaptability. The data synchronization module completes offline operation data retransmission through a consistency check algorithm after network recovery, synchronously updating local resource versions and cache status, effectively avoiding data loss and version conflicts. Overall, this improves the offline availability, update efficiency, and data reliability of the ride-hailing app, providing users with a smoother and more stable service experience. Thus, it solves the problems of low update efficiency, lack of degradation capabilities, and extremely poor offline availability in existing technologies.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the existing solution for an H5 offline version and incremental resource update system of a designated driver app according to an embodiment of this application; Figure 2 This is a schematic diagram of the structure of an H5 offline system and incremental resource update system for a designated driver app according to an embodiment of this application; Figure 3 This is a schematic diagram of a data acquisition module provided according to an embodiment of this application; Figure 4 This is a schematic diagram of an incremental update module provided according to an embodiment of this application; Figure 5 This is a schematic diagram of an offline cache management module according to an embodiment of this application; Figure 6 This is a schematic diagram of a network adaptive scheduling module according to an embodiment of this application; Figure 7 This is a schematic diagram of a data synchronization module provided according to an embodiment of this application; Figure 8This is a schematic diagram of an H5 offline system and incremental resource update system for a designated driver app according to an embodiment of this application; Figure 9 This is a flowchart illustrating a method for implementing an H5 offline version and incremental resource update system for a ride-hailing app according to an embodiment of this application; Figure 10 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] The following description, with reference to the accompanying drawings, illustrates an H5 offline and incremental resource update system for a ride-hailing app, based on an embodiment of this application. Addressing the common problem of insufficient predictability mentioned in the background section, this application provides an H5 offline and incremental resource update system for a ride-hailing app. This system achieves efficient and stable H5 offline processing and resource management capabilities through multi-module collaboration. The data acquisition module provides accurate data for resource version management and scenario adaptation, ensuring the targeted nature of subsequent operations. The incremental update module significantly reduces resource update traffic consumption and time costs, improving update efficiency through version list comparison and breakpoint resume download in chunks. The offline cache management module relies on ServiceWorker for hierarchical caching of core resources and dynamically eliminates redundant resources. The system constructs a highly available offline resource pool to ensure the stable operation of core chauffeur functions in weak or no-network scenarios. The network adaptive scheduling module intelligently switches strategies based on real-time network conditions, promptly updating and activating full functionality when online, and triggering function degradation when offline to ensure basic service availability, significantly enhancing application environment adaptability. The data synchronization module, after network recovery, completes offline operation data retransmission through a consistency check algorithm, synchronously updating local resource versions and cache states, effectively avoiding data loss and version conflicts. Overall, this improves the offline availability, update efficiency, and data reliability of the chauffeur app, providing users with a smoother and more stable service experience. This solves the problems of low update efficiency, lack of degradation capabilities, and extremely poor offline availability in existing technologies.

[0020] Figure 2 This is a schematic diagram of the structure of an H5 offline system and incremental resource update system for a designated driver app provided in an embodiment of this application.

[0021] This application provides an H5 offline version and incremental resource update system for a designated driver app. The system 10 includes: Data acquisition module 100, incremental update module 200, offline cache management module 300, network adaptive scheduling module 400, and data synchronization module 500.

[0022] The system comprises the following modules: Data Acquisition Module 100, Incremental Update Module 200, and Data Acquisition Module 500. The Data Acquisition Module 100 collects core H5 page resource data, interface configuration information, and characteristic data of the chauffeur service app. Incremental Update Module 200, which builds a local resource version list and performs differential verification with the server-side resource version list. If version differences exist, an incremental update process is triggered, using a breakpoint resume engine to download and verify the different resources in chunks. Offline Cache Management Module 300, which uses ServiceWorker to build an offline cache management system, caches core H5 resources hierarchically according to chauffeur service scenario priority, generates an offline available resource pool, and establishes a dynamic cache eviction mechanism. Network Adaptive Scheduling Module 400, which uses an adaptive scheduling strategy based on network status, synchronously updates resources and activates full H5 functionality when online, and enables the offline available resource pool when offline, triggering a mechanism to retain core chauffeur service functions and degrade non-core functions. Data Synchronization Module 500, which, after network recovery, synchronizes chauffeur service operation data from offline status to the server using a data consistency verification algorithm, while simultaneously updating the local resource version list and cache status.

[0023] It is understood that this embodiment achieves efficient and stable H5 offline processing and resource management capabilities through multi-module collaboration. The data acquisition module provides a precise data foundation for resource version management and scenario adaptation, ensuring the targeted nature of subsequent operations. The incremental update module significantly reduces resource update traffic consumption and time costs, improving update efficiency through version list comparison and breakpoint resume download in chunks. The offline cache management module relies on ServiceWorker to hierarchically cache core resources and dynamically eliminate redundant content, building a highly available offline resource pool to ensure the stable operation of the core functions of the ride-hailing service in weak or no-network scenarios. The network adaptive scheduling module intelligently switches strategies based on real-time network status, promptly synchronizing and activating full functionality when online, and triggering function degradation when offline to ensure basic service availability, significantly enhancing application environment adaptability. The data synchronization module completes offline operation data retransmission through a consistency check algorithm after network recovery, synchronously updating local resource versions and cache status, effectively avoiding data loss and version conflicts. Overall, this improves the offline availability, update efficiency, and data reliability of the ride-hailing app, providing users with a smoother and more stable service experience. Thus, it solves the problems of low update efficiency, lack of degradation capabilities, and extremely poor offline availability in existing technologies.

[0024] In this embodiment of the application, the data acquisition module 100 includes: Figure 3As shown, there are three units: resource list collection unit, interface configuration collection unit, and scene feature collection unit.

[0025] The resource list collection unit periodically retrieves the version list of H5 static resources from the server and records the hash value version number of each file; the interface configuration collection unit specifically collects the configuration information of API interfaces related to the chauffeur service; and the scene feature collection unit collects specific environmental data for chauffeur services in real time and dynamically identifies business priorities.

[0026] It should be noted that H5 static resources refer to various static files in an HTML5 page, such as HTML files, CSS stylesheets, JavaScript scripts, images, etc.

[0027] It is understood that this application embodiment uses a resource list collection unit to periodically obtain the H5 static resource version list and record the file hash value version number, providing accurate difference identification basis for subsequent incremental updates and avoiding traffic waste and update delays caused by full downloads; it uses an interface configuration collection unit to specifically collect the API interface configuration information of the chauffeur service, ensuring that the system can accurately adapt to the core business interfaces, laying the foundation for interface request processing and data synchronization in offline mode; and it uses a scene feature collection unit to collect chauffeur-specific environment data in real time and dynamically identify business priorities, enabling the system to prioritize caching and function protection of resources in high-priority scenarios, thereby significantly improving the adaptability of H5 offline, the efficiency of incremental updates, and the availability of the core chauffeur service in complex network environments, and optimizing the user experience.

[0028] In this embodiment of the application, the incremental update module 200 includes: Figure 4 The unit shown includes a list acquisition unit, a difference comparison unit, an incremental download unit, and a list synchronization unit.

[0029] The system consists of several components: a list acquisition unit, a list of resource versions, and a list of hash values ​​for each H5 static file. A differential comparison unit compares the hash values ​​of the online and local lists item by item to accurately identify newly added, modified, or deleted files and performs file-level change detection. An incremental download unit, based on the differential list, uses a breakpoint resume engine to download only changed resources, supporting recovery from the point of interruption after network interruption. Finally, a list synchronization unit atomically updates the local list after downloading.

[0030] It is understood that the inventory acquisition unit in this application actively acquires and records the latest resource version list from the server, providing an accurate basis for version comparison; the differential comparison unit uses hash values ​​for precise file-level comparison, which can quickly identify the changed file list, ensuring the accuracy of the update range and avoiding unnecessary resource downloads; the incremental download unit, based on the differential list and using a breakpoint resume engine, only downloads changed resource blocks and can resume from the breakpoint when the network fluctuates, greatly saving user traffic and improving the update success rate; the inventory synchronization unit updates the local inventory through atomic operations, ensuring the consistency between the local resource version status and the downloaded content, and preventing program errors caused by incomplete updates. The entire module works together to significantly improve update efficiency and network robustness while ensuring resource freshness.

[0031] For example, consider the scenario of a designated driver updating H5 map resources before starting their shift in the early morning: the user's local cache is version v2.1, while the server has released a version v2.2 resource list—the online list shows: index.html (hash changed from a3f8b9 to c5d2e1, size increased from 1.2KB to 1.5KB, fixing map loading delay), a new map-utils.js (hash f7g8h9, size 8KB, optimizing path planning algorithm), and the deletion of old-splash.png (hash i0j1k2, size 5KB, replaced with the new brand logo). The hashes of the remaining 12 files, such as style.css (hash l3m4n5, 3KB), remain unchanged. The list retrieval unit actively pulls the v2.2 list (containing the hashes and version numbers of 18 files), and the differential comparison unit verifies each item and finds: index.html (hash changed, needs to be updated +0.3KB), map-utils.js (new, needs to be downloaded 8KB), and old-splash.png (deleted, awaiting cleanup). The incremental download unit then initiates: first downloading index.html (1.5KB, completed in 500ms), then interrupting the download of map-utils.js at 4KB due to signal fluctuations. After the engine records the progress and the network recovers, the remaining 4KB is resumed (total time 2.1 seconds, saving 73% of time compared to a full download). After all files are downloaded and verified to be correct, the manifest synchronization unit atomically replaces the local manifest with v2.2 and deletes the remaining old-splash.png. The end user only downloads 8.3KB of incremental resources (saving 48.7% of traffic compared to the full 16.2KB), and the H5 map seamlessly loads the latest features when the vehicle departs, with no noticeable difference throughout the process.

[0032] In this embodiment of the application, the offline cache management module 300 includes: Figure 5As shown, the ServiceWorker management unit, resource hierarchical caching unit, offline resource pool maintenance unit, and cache dynamic eviction unit are all included.

[0033] The ServiceWorker management unit is responsible for registering and activating ServiceWorkers, intercepting resource requests from H5 pages and executing caching priority strategies, while also managing the caching logic for multiple versions of resources. The resource hierarchical caching unit classifies and caches H5 static resources according to the priority of the ride-hailing scenario. The offline resource pool maintenance unit integrates the effective resources after hierarchical caching, builds a structured offline available resource pool, and updates the resource status in real time. The cache dynamic eviction unit automatically evicts low-priority resources that have not been used for a long time based on resource usage frequency, cache validity period, and device storage capacity.

[0034] It is understood that this application embodiment completes the registration and activation of ServiceWorkers through the ServiceWorker management unit, intercepts H5 page resource requests and executes a caching priority strategy, and effectively manages multi-version resource caching logic, providing basic technical support for offline resource access and ensuring the efficiency and stability of resource loading. With the help of a resource hierarchical caching unit, H5 static resources are classified and cached according to the priority of the ride-hailing scenario, prioritizing the availability of resources for high-core business scenarios such as order taking, navigation, and payment. The offline resource pool maintenance unit integrates the effective resources after hierarchical caching, constructs a structured offline available resource pool, and updates the resource status in real time, realizing the orderly management and rapid access of offline resources. The cache dynamic eviction unit automatically evicts low-priority resources that have not been used for a long time based on resource usage frequency, cache validity period, and device storage capacity, avoiding cache redundancy that occupies too much storage space and ensuring smooth device operation. Overall, this module not only significantly improves the offline availability and loading speed of the ride-hailing app's H5 page, ensuring the normal operation of core businesses in unstable network or offline states, but also optimizes device resource utilization efficiency, thereby comprehensively improving the user experience.

[0035] For example, in the H5 offline and incremental resource update system of a ride-hailing app, the ServiceWorker management unit automatically registers and activates upon the user's first opening. Subsequent access to core functions intercepts resource requests such as JS, CSS, and location components, implementing a caching priority strategy. The core static resource cache hit rate reaches over 95%, and the offline page loading time is reduced from 3 seconds to within 450ms. This unit supports caching two resource versions simultaneously, distinguishing between old and new resources using hash values. During incremental updates, only the differences are downloaded, reducing the size of the differential package by 85% compared to the full package and shortening update time by 70%. The resource tiered caching unit divides static resources according to scenario priority: core scenarios (ordering, payment, real-time location) have a total resource size of 3MB, which is forcibly cached upon first access; secondary scenarios (historical orders, driver information) have 5MB of resources, which are silently cached in the background for 30 seconds only in Wi-Fi environments; non-core scenarios (marketing activities, help center) have 8MB of resources, which are cached on demand with a 7-day validity period. The offline resource pool maintenance unit integrates effective resources according to functional modules, synchronizing resource status every 5 minutes. The pass rate for valid resource integrity verification reaches 99.8%, ensuring that core functional resources are not missing when offline. The cache dynamic eviction unit adopts the LRU algorithm. When the device cache occupies nearly 80% of the browser's 50MB quota for a single site, it automatically cleans up low-priority resources that have not been used for 30 days. A single eviction can release 2-5MB of storage space, while retaining the top 3 most frequently used core caches. This ensures that the availability of the core function of chauffeur services remains at 98% even in environments without network access, such as underground parking garages.

[0036] In this embodiment of the application, the network adaptive scheduling module 400 includes, as follows: Figure 6 As shown, the network status detection unit, policy decision-making unit, and function scheduling unit are included.

[0037] The network status detection unit is responsible for monitoring the network connection status of the device in real time, and providing basic data input for adaptive scheduling through heartbeat mechanism or API response timeout judgment; the strategy decision unit dynamically selects the optimal strategy based on the network status, and prioritizes triggering the incremental update resource synchronization process and activating the full H5 function when online, and automatically switches to the degradation mode when offline; the function scheduling unit specifically executes the strategy decision, coordinates resource download and function activation when online, and activates the offline available resource pool when offline, prioritizes the retention of the core chauffeur service function, and degrades non-core functions.

[0038] It is understood that the network status detection unit in this application embodiment senses the network status in real time through a heartbeat mechanism or API response timeout, providing accurate underlying data input for strategy formulation and avoiding strategy deviations caused by network misjudgments. The strategy decision unit dynamically switches to the optimal mode based on the detection results—when online, it prioritizes the synchronization of incremental resource updates and activates the full H5 functionality to ensure users receive the latest services in a timely manner; when offline, it quickly switches to a degraded mode to avoid invalid network requests in advance. The function scheduling unit acts as the execution hub, coordinating resource downloads and function activation when online to ensure seamless integration of updates and usage; when offline, it accurately activates the offline available resource pool, prioritizing the retention of core functions such as navigation and order taking, while degrading non-core functions such as location marking and history records, thus ensuring the availability of basic services and avoiding lag or crashes caused by resource redundancy.

[0039] For example, after a user opens the designated driver app, the network status detection unit monitors the network every 10 seconds. When it detects that the device is connected to a 4G network (signal strength -75dBm, download speed 1.2Mbps), it is determined to be online. The policy decision unit triggers an incremental update (version v2.1.1, package size 35KB, including a 12KB driver location display JS file and a 23KB new activity banner image). The function scheduling unit completes the background download in 2.8 seconds, updating the local 415KB offline resource pool to 450KB and activating the full functionality (including order placement, real-time positioning with a location error ≤5 meters, driver information display, etc.). When the user drives deep into the underground parking garage, if the detection unit does not receive a heartbeat response from the server for 3 consecutive times (with an interval of 5 seconds) (timeout threshold of 3 seconds) and the API request takes more than 5 seconds. Upon detecting an offline status, the system immediately switches to downgrade mode, activating a local 415KB offline H5 resource pool (including 120KB core order placement HTML, 85KB CSS style files, and 210KB core JS logic). Core functions such as order placement, cached GPS positioning, and local driver contact are retained, while non-core functions such as historical order query and points redemption are hidden, and an offline status is displayed. Three minutes later, the user drives out of the garage, and the network returns to 4G (signal strength -60dBm, download speed 2.5Mbps). The system automatically triggers an incremental update to version v2.1.2 (package size 42KB, including 18KB payment process JS, 10KB evaluation page CSS, and 14KB icon resources), completing the update in 1.7 seconds to bring the resource pool to 492KB. All functions are simultaneously restored, and a message indicating that the network and functions have been updated is displayed.

[0040] In this embodiment of the application, the data synchronization module 500 includes, as follows: Figure 7 As shown, there are an offline data collection unit, a data verification and synchronization unit, and a status update unit.

[0041] The offline data collection unit is responsible for continuously collecting ride-hailing operation data during offline periods and temporarily storing it in a local queue; the data verification and synchronization unit verifies the integrity of offline data through a data consistency verification algorithm after the network is restored, and then securely transmits it to the server; the status update unit atomically updates the local resource version list and cache status after successful synchronization, synchronizes changes on the server side, and cleans up the processed data.

[0042] It is understood that, in this embodiment of the application, the offline data collection unit continuously collects chauffeur operation data during offline periods and temporarily stores it in a local queue to ensure that critical business data such as driver order acceptance and order completion are not lost when the network is interrupted; with the help of the data verification and synchronization unit, the integrity of the offline data is verified by the data consistency verification algorithm after the network is restored, and then it is securely transmitted to the server to avoid data corruption or duplicate submission; after successful synchronization, the status update unit atomically updates the local resource version list and cache status, synchronizes server-side changes and cleans up processed data, ensuring that the local and server data statuses are consistent, while releasing redundant storage resources.

[0043] For example, if a designated driver experiences a 25-minute network outage after accepting an order in an underground parking garage, the system's offline data collection unit continuously collects data through the IndexedDB local queue, including the start and end times of a complete designated driver order, 238 GPS trajectory points (collected every 5 seconds), and 3 fee adjustment details, with temporary storage occupying 1.2MB of space. When the driver leaves the garage and the 4G network is restored, the data verification and synchronization unit immediately verifies the data integrity using the MD5 consistency check algorithm (100% pass rate), and securely transmits the compressed 1.5MB of data to the server within 3.8 seconds through an encrypted channel. The status update unit then atomically updates the local resource version from v4.6.2 to v4.6.3, synchronizes the order status on the server to "completed," and cleans up the 1.0MB of processed offline data locally, ensuring that the local cache and server data are completely consistent.

[0044] This application proposes an H5 offline and incremental resource update system for a ride-hailing app. Through multi-module collaboration, it achieves efficient and stable H5 offline processing and resource management capabilities. The data acquisition module provides accurate data for resource version management and scenario adaptation, ensuring targeted subsequent operations. The incremental update module significantly reduces resource update traffic consumption and time costs by comparing version lists and using breakpoint resume and segmented downloads, improving update efficiency. The offline cache management module relies on ServiceWorker to hierarchically cache core resources and dynamically eliminate redundant content, building a highly available offline resource pool to ensure the stable operation of core ride-hailing functions in weak or no-network scenarios. The network adaptive scheduling module intelligently switches strategies based on real-time network status, promptly synchronizing and activating full functionality when online, and triggering function degradation when offline to ensure basic service availability, significantly enhancing application environment adaptability. The data synchronization module completes offline operation data retransmission through a consistency check algorithm after network recovery, synchronously updating local resource versions and cache status, effectively avoiding data loss and version conflicts. Overall, it improves the offline availability, update efficiency, and data reliability of the ride-hailing app, providing users with a smoother and more stable service experience. This solves the problems of low update efficiency, lack of degradation capability, and extremely poor offline availability in existing technologies.

[0045] The following will illustrate a specific embodiment of an H5 offline system and incremental resource update system for a ride-hailing app, including: On Friday evening at 8:30 PM, Xiao Wang, a 28-year-old internet professional, and three colleagues finished their dinner at a Hunan restaurant in the city center. Having consumed about 100ml of baijiu (a type of Chinese liquor), he needed to use a ride-hailing app to call a driver to take him back to his apartment complex in the south of the city. When he pulled out his phone and opened the "XXX Ride-Hailing" app, he had no idea how the H5 offline system with incremental resource updates would solve the network bottleneck in the basement of the shopping district, allowing him to successfully place a ride order even with a no-signal environment.

[0046] The moment the app launches, the system first enters the initialization process, such as... Figure 8As shown, after the flowchart starts with "Starting the chauffeur app," the first node is the check "Has ServiceWorker (SW) been registered?" Since this is Xiao Wang's first time using the latest version of "[App Name]" on this newly purchased Android phone, the ServiceWorker (SW) responsible for offline cache management has not yet been registered. Therefore, the process automatically proceeds to the "No" branch, triggering the "Register ServiceWorker" operation. This is one of the core actions of the offline cache management module: building an underlying offline cache system through ServiceWorker is equivalent to "pre-installing" a local resource scheduling "manager" for the app. Subsequent resource caching and offline access will be based on this "manager," and the entire registration process is completed silently in the background. Xiao Wang's interface only displays the "[App Name], Safe and Secure" launch page.

[0047] After registration, the system enters the "Initialize Core Resources (Single Page, Order Page)" step. At this time, the data collection module starts working simultaneously: it first collects the core H5 page resource data of the ride-hailing app—including the layout code of the order form page and the interaction logic file of the driver matching page; then it captures interface configuration information, such as the request parameters of the driver location interface and the encryption rules of the order submission interface; finally, it also collects the characteristic data of the current ride-hailing scenario, such as the ride-hailing demand density of the "Starlight Business District" during the current time period (8-10 pm) (the system backend shows that there are 12 ride-hailing orders in the business district within 10 minutes), and the proportion of "comfort-type ride-hailing" service preferred by users in the nighttime scenario (approximately 65%). This data will not only be synchronized to the server for subsequent resource optimization, but also provide a basis for the "resource hierarchical caching" of the offline cache management module—the core order and driver matching pages will be marked as "high priority" and will be given priority in entering the offline resource pool.

[0048] Next, the system executes the "Load Local Resource List" operation, a fundamental action of the incremental update module: it generates a "Resource Version List" locally, recording the version numbers of all H5 resources currently cached on the device, such as the order page version being "V2.3.0" and the driver location interface version being "V1.8.2". Subsequently, the incremental update module sends a request to the "XXX Chauffeur" server to "Get Server Resource Version List"—at this point, the server's list shows that a resource update was just completed at 10:00 AM that day: the order page's UI style was optimized (version upgraded to "V2.3.1"), and the driver location interface's response speed was also adjusted (version upgraded to "V1.8.3").

[0049] The system then entered the "Version Comparison?" node, comparing the local list with the server list one by one. It found two version differences, thus the process moved to the "Differences Exist" branch, triggering the "Incremental Update (Resume Download)" process of the incremental update module. At this time, Xiao Wang was standing in the basement passageway outside the Hunan restaurant. His phone signal bar showed "1 bar 4G," indicating extremely unstable network transmission. With a traditional full update, a resource package of about 2MB might be repeatedly downloaded due to network interruptions. However, the incremental update module's "Resume Download Engine" solved this problem: it divided the differing resources (only a 200KB order page style file + a 50KB interface configuration file) into five 50KB small data packets. After each data packet was downloaded, it automatically verified the MD5 value to ensure integrity. During this period, the network was briefly interrupted twice (each time for about 10 seconds), and the engine automatically resumed downloading from the breakpoint after the network was restored, instead of restarting the full download. The entire incremental update process took only 1 minute and 20 seconds, and Xiao Wang's interface only displayed a simple "Optimizing experience, please do not exit" message.

[0050] After the incremental update is complete, the system performs an "update local resource list" operation, updating the order page version to "V2.3.1" and the interface version to "V1.8.3" in the local list. It then proceeds to the "load local cached H5 page" step—at this point, the app's main interface has already loaded, displaying the core function box for "enter departure / destination." However, just as Xiao Wang is about to enter his address, he enters the basement parking lot, where his cell phone signal completely disappears. The system then enters the "network status?" judgment node, detecting that he is currently in an "offline" state, thus triggering the network adaptive scheduling module's strategy.

[0051] As designed, the app will load full H5 functionality when online, including payment method selection (WeChat, Alipay, bank card), driver historical service rating graph, trip sharing to social media platforms, etc. However, when offline, the network adaptive scheduling module will activate the "offline available resource pool" generated by the offline cache management module, and trigger the "core functions are retained, non-core functions are downgraded" mechanism: core functions such as order filling, basic driver matching (based on cached nearby driver preload data), and basic trip pricing remain available, while non-core functions such as coupon redemption, driver rating details, and trip sharing will display a gray message "Current network is poor, function is temporarily unavailable".

[0052] Xiao Wang didn't notice the change in network status. He successfully entered the departure point "Xingguang Business District Hunan Restaurant" on the order page (the system automatically completed the address based on cached geolocation data), selected the destination "Chengnan Community," and checked his usual "Comfort Driver" option—the interface response speed for these operations was almost indistinguishable from when he was online, because all core resources came from the offline cache pool. When he clicked the "Call Driver" button, the system did not directly send a request to the server, but instead temporarily stored the order information in the local database through the offline cache management module, while a prompt popped up: "Your order has been pre-placed; a driver will be matched immediately after the network is restored."

[0053] Ten minutes later, Xiao Wang reached the ground level of the basement exit. His phone reconnected to a full-strength 4G network. The system detected the network status change in real time and entered the "Network restored?" checkpoint. The result was "yes," and the data synchronization module was immediately activated. This module first uses a "data consistency verification algorithm" to compare the locally cached order data (departure point, destination, service type) with the real-time data on the server. After confirming that the order information has not been tampered with and that there are still available "comfort-type designated drivers" in the current business district, the order data is synchronized to the server. At the same time, it also synchronizes the latest status of the local resource version list to the server and updates the locally cached "offline available resource pool" to ensure that subsequent operations can be performed based on the latest resources.

[0054] In less than 30 seconds, Xiao Wang received a notification on his phone: "Driver Zhang has been matched for you, 800 meters away, estimated arrival in 5 minutes." Simultaneously, the full functionality loaded: he could view Driver Zhang's real-time location (based on the updated location interface), choose WeChat Pay as the payment method, and even see Driver Zhang's historical service rating (4.9 stars). Twenty minutes later, the trip ended, and Xiao Wang completed the online payment on the app, submitting a review stating "Service was prompt, driving was smooth"—all these operations were based on the latest incrementally updated resources and online synchronized data. The entire process neither suffered from network issues nor excessive data consumption due to resource updates (only 250KB, far less than the 2MB of a full update).

[0055] In summary, this application's embodiment transforms the originally abstract technical system into a user-perceptible experience optimization: Xiao Wang neither faced the predicament of "function freeze and inability to place orders" due to network interruption, nor did he consume excessive traffic due to resource updates. Instead, he completed critical operations offline using core functions and seamlessly resumed full service after going online. This directly alleviated the user's anxiety about "service unavailability in special environments," transforming the value of the technical solution from "backend capabilities" to "practical convenience." For users, it provides a safety net of "network conditions not affecting the initiation of core services." For the ride-hailing platform, it reduces order loss in weak network scenarios, lowers the traffic cost of resource distribution through incremental updates, and transforms the "stability and efficiency" of the technical system into actual service competitiveness through module collaboration (accurate data collection matching scenarios, offline caching ensuring basic availability, and synchronous modules supplementing operational effectiveness). This allows the abstract system design to truly be implemented as a scenario-based problem solution.

[0056] Next, referring to the accompanying drawings, a method for H5 offline implementation and incremental resource update system of a designated driver app, according to an embodiment of this application, is described.

[0057] like Figure 9 As shown, the method for implementing an offline H5 version and incremental resource update system for a ride-hailing app includes the following steps: In step S101, the core H5 page resource data, interface configuration information, and chauffeur scenario feature data of the chauffeur app are collected.

[0058] Understandably, the implementation of this application can comprehensively identify and define the core functions and business logic of the App, providing basic data support for the subsequent construction of offline caching and incremental update systems. By analyzing this data, the system can accurately distinguish between core and non-core resources, optimize caching strategies, and ensure that key functions (such as order creation and location tracking) remain available offline. At the same time, interface configuration information enables H5 pages to simulate network requests, improving the offline experience, while scene feature data helps to achieve intelligent degradation and data synchronization, ultimately enhancing the stability of the App and the user experience.

[0059] In step S102, based on the core H5 page resource data, interface configuration information, and chauffeur scenario feature data of the chauffeur app, a local resource version list is constructed and compared with the server-side resource version list for differential verification. If there is a version difference, an incremental update process is triggered. The differential resources are downloaded and verified in blocks through the breakpoint resume engine. At the same time, an offline cache management system is constructed based on ServiceWorker, and the core H5 resources are cached hierarchically according to the priority of the chauffeur scenario, generating an offline available resource pool and establishing a dynamic cache eviction mechanism.

[0060] ServiceWorker is a script that runs in the background of the browser and can intercept and process network requests to enable functions such as offline caching and message push.

[0061] It should be noted that resumeable download means that if the file transfer is interrupted due to network or other reasons, the transfer can continue from where it was interrupted, rather than starting all over again.

[0062] It is understood that this application embodiment, acting as a proxy layer between the browser and the network, intercepts H5 resource requests and manages offline cache to provide efficient and stable offline services for ride-hailing scenarios. It can prioritize H5 resources according to core functions such as navigation and ride-hailing, building an offline available resource pool to ensure users can still use critical functions even with weak or no network connections. It supports background preloading and version verification of incremental resources, automatically updating the cache when online and quickly responding to requests offline to avoid duplicate downloads. Simultaneously, it combines a dynamic cache eviction mechanism to intelligently clean up redundant resources to optimize storage. Through event-driven lifecycle management and request interception capabilities, it achieves reliable offline functionality, flexible resource updates, and efficient utilization of storage space, significantly improving the continuity and stability of the user experience in ride-hailing scenarios.

[0063] For example, in the H5 offline system of this ride-hailing app, the specific operational data of ServiceWorker is as follows: Upon initial startup, it pre-caches high-priority core resources—the navigation page JS package (2.3MB), the order confirmation page HTML template (800KB), the real-time location interface script (500KB), and basic CSS (300KB)—totaling 3.9MB of resources, in CacheStorage and marked as version v1.2. When the user accesses the navigation page offline, ServiceWorker directly returns resources from the cache, compressing the page loading time from 780ms in the online state to 110ms, ensuring the core function response meets the standards. When a server resource update (v1.3 version) is detected, after comparing the version list, only a 150KB incremental patch for the navigation JS needs to be downloaded (a full update requires 2.3MB). After verifying the hash consistency through SHA-256, it is written to the CacheStorage / v1.3 directory; the background update takes 220ms, which is imperceptible to the user. During the offline period, the user submitted three orders (including location coordinates, trip time, and other data). After the network was restored, ServiceWorker triggered data synchronization. By comparing the difference between the local operation logs and the server timestamps using a verification algorithm, the three data entries were 100% successfully synchronized. Simultaneously, the old version v1.2 cache was cleared, freeing up 4.2MB of space. This process, through ServiceWorker's precise cache control and data synchronization capabilities, achieved an offline availability rate of 99.6% for the core chauffeur service functions and reduced online update time by 87%.

[0064] In step S103, according to the cache dynamic eviction mechanism, the network status adaptive scheduling strategy is used to synchronously update resources incrementally and activate the full H5 function when online, and to enable the offline available resource pool when offline, triggering the mechanism of retaining the core functions of the chauffeur service and downgrading the non-core functions. When the network is restored, the chauffeur service operation data in the offline state is synchronized to the server through the data consistency verification algorithm, and the local resource version list and cache status are updated at the same time.

[0065] Incremental updates only update the changed resources, rather than updating the entire application or page, thus saving bandwidth and time.

[0066] It is understood that by downloading only the changed resource blocks instead of the complete resource package, the embodiments of this application greatly reduce the amount of data transmission. This not only significantly reduces the user's traffic consumption and improves the speed and efficiency of resource updates, but also enables rapid deployment and silent updates, thereby ensuring that users can obtain a smooth and stable core chauffeur service experience even in weak or intermittent network environments, and reducing the bandwidth pressure on the server.

[0067] For example, the "Order Confirmation Page" (core H5 page) of a ride-hailing app has a local resource version list that records the core JS file (order-confirm.js) version as v2.3.0 and the CSS file (order-confirm.css) version as v1.8.0, with no additional component resources; when the server releases a new version, the version list is updated to order-confirm.jsv2.4.0 (fixing the driver location positioning error issue) and a new coupon-render.jsv1.0.0 (supporting the new coupon display component) is added, while the CSS version remains the same. When a user opens the app, the ServiceWorker backend triggers a version check. It detects "JS version differences" and "new resources" between the local and server lists, immediately initiating an incremental update process: Using a breakpoint-resume download engine, order-confirm.jsv2.4.0 (1.2MB) is divided into three 400KB chunks, and coupon-render.jsv1.0.0 (800KB) is divided into two 400KB chunks. Chunk 1 is downloaded first; if network fluctuations cause an interruption, downloading resumes directly from chunk 2 after network recovery, without needing to re-download completed portions. After all chunks are downloaded, MD5 verification ensures resource integrity. The ServiceWorker then caches these two resources in a high-priority cache pool for the "ride-hailing order scenario" (because order confirmation is a core process, the cache is not dynamically evicted), and simultaneously updates the local version list. When online, the ServiceWorker intercepts resource requests the next time a user enters the order confirmation page, returning the cached new version of resources to ensure the use of the fixed location function and the newly added coupon display. If the user opens the page offline, the ServiceWorker will detect the network disconnection and automatically enable the existing cached v2.3.0 version JS and v1.8.0 version CSS, retaining the core order submission and driver information viewing functions, only hiding the uncached coupon component. When the network is restored, the system automatically synchronizes the order data submitted by the user offline (such as the selected vehicle model and remarks) to the server through a data consistency verification algorithm, and at the same time reconfirms the resource cache status to ensure that the local cache is consistent with the server.

[0068] The method for H5 offline processing and incremental resource update system of a ride-hailing app proposed in this application achieves efficient and stable H5 offline processing and resource management capabilities through multi-module collaboration. The data acquisition module provides accurate data foundation for resource version management and scenario adaptation, ensuring the targeted nature of subsequent operations. The incremental update module significantly reduces resource update traffic consumption and time costs by comparing version lists and using breakpoint resume and segmented downloads, thus improving update efficiency. The offline cache management module relies on ServiceWorker to hierarchically cache core resources and dynamically eliminate redundant content, building a highly available offline resource pool to ensure the stable operation of core ride-hailing functions in weak or no-network scenarios. The network adaptive scheduling module intelligently switches strategies based on real-time network status, promptly synchronizing and activating full functionality when online, and triggering function degradation when offline to ensure basic service availability, significantly enhancing application environment adaptability. The data synchronization module completes offline operation data retransmission through a consistency check algorithm after network recovery, synchronously updating local resource versions and cache status, effectively avoiding data loss and version conflicts. Overall, this improves the offline availability, update efficiency, and data reliability of the ride-hailing app, providing users with a smoother and more stable service experience. This solves the problems of low update efficiency, lack of degradation capability, and extremely poor offline availability in existing technologies.

[0069] The following will illustrate a method for implementing an H5 offline version and incremental resource update system for a ride-hailing app through a specific embodiment, including: The technical team of a ride-hailing platform recently launched an offline H5 application and incremental resource update system. Its core value was fully validated in a late-night order from a ride-hailing driver. This service process, which took place in the suburbs of the city, not only demonstrated the stability of H5 applications in weak network environments, but also confirmed the effectiveness of the technical solution through specific data.

[0070] The story began two weeks ago during the resource preparation phase. The platform's technical team conducted a full data collection for the core H5 business: core H5 page resources covered high-frequency modules such as the homepage, order page, and navigation page, including the HTML skeleton (approximately 800KB), JS logic scripts (such as the positioning engine 1.2MB and the pricing rule module 800KB), CSS style files (responsive layout adapted to multiple devices, totaling 1.5MB), and icon resources (vehicles, emergency contacts, etc., approximately 200KB), with the original resource package totaling 4.7MB; interface configuration information covered the URLs of 12 key interfaces such as order creation, real-time positioning, and route planning, AES-256 encryption rules, and a timeout policy of 3 retries; the characteristic data of the designated driver scenario was obtained through data tracking statistics—orders accounted for 45% at night (22:00-6:00), 30% in suburban areas, and 18% in rainy or snowy weather, with users' core needs focusing on "fast order response," "offline navigation availability," and "unimpeded emergency communication." Based on this, the server built a global resource version list (version number v1.2.5), recording the SHA-256 hash value, file size (1.2MB), and last update time of each file. At this time, the version list cached locally on the driver's phone remained at v1.2.3, where the local hash of the navigation page CSS file nav.css was d5e2...f91a, while the server-side version of this file had been updated to d5e2...f91b (fixing the issue of blurred route color markings in rainy and snowy weather).

[0071] When a driver opens the app to accept an order, the system automatically triggers a version difference check: the local list (v1.2.3) and the server list (v1.2.5) are compared item by item, revealing three high-priority differences—nav.css (affecting nighttime navigation style), order-detail.js (fixing offline order amount calculation errors), and emergency-contact.png (emergency contact icon). Considering the unstable network in suburban areas, the system calls the breakpoint resume engine to start an incremental update: the three difference resources are divided into blocks of 100KB / block (total difference resources are only 780KB), prioritizing the download of the first three blocks (300KB) of nav.css. However, when downloading the second block, the signal suddenly drops to 2G (download speed <10KB / s), and the engine immediately records the 200KB of download progress; after 5 minutes, the signal recovers, and the download continues from the third block. Finally, all blocks are completed and pass the hash check, and the local version list is upgraded to v1.2.5. The entire process takes 47 seconds, and the driver is unaware of it. After downloading, the system builds an offline caching system based on ServiceWorker, managing it hierarchically according to scenario priority: Level 1 cache (forced pre-caching, accounting for 50%, approximately 235MB): includes homepage HTML (800KB), order page JS (1.2MB), and basic location logic (800KB), ensuring that it can respond to orders after a cold start; Level 2 cache (scenario-related cache, accounting for 30%, approximately 141MB): stores night mode styles (dark-mode.css, 200KB), rain and snow weather navigation prompts (rain-nav.png, 150KB), and emergency call page (emergency.html, 500KB), matching high-frequency weak network scenarios; Level 3 cache (low-frequency resources, accounting for 20%, approximately 94MB): stores low-frequency access content such as historical order details page (order-history.html, 300KB) and driver evaluation page (driver-review.html, 400KB). Upon inspection, it was found that the third-level resource order-history.html, which had not been accessed for nearly 30 days in the driver's cache, was marked as an "evictable object" due to the LRU algorithm. The system automatically released the 80MB of space it occupied to store the newly downloaded nav.css and order-detail.js, ensuring sufficient capacity for core resources.

[0072] At 1:30 AM, the driver entered the suburbs, where the signal fluctuated further (2G → no service alternation). The app detected a "weak network - offline" state and immediately switched to the offline available resource pool: Core functions are retained: The order acceptance page relies on the first-level cached HTML and JS, loading within 0.8 seconds; after the driver clicks "Confirm Order Acceptance," the order information (including order ID, timestamp, and origin coordinates) is temporarily stored via localStorage and marked as "offline operation"; the navigation page calls the second-level cached offline map tiles of the Shanghai suburbs (pre-downloaded 1:10000 road data, approximately 50MB), combined with the phone's GPS module to achieve basic route planning (no real-time traffic conditions, but route deviation <50 meters); the emergency contact page (first-level cache) directly calls the system phone interface, dialing the preset number within 3 seconds. Non-core functions are downgraded: the order details page is eliminated due to the third-level cache and cannot load historical trajectory maps, displaying "Current network unavailable, some information will be synchronized after network connection"; the evaluation page hides the "Submit Evaluation" button, only displaying the text "Offline evaluation will be uploaded after recovery."

[0073] At 2:10 AM, the driver arrived at the destination, and the phone reconnected to the 5G network (download speed restored to 8MB / s). The system triggered network adaptive scheduling: offline data synchronization: the "data consistency verification algorithm" was called—the hash value (timestamp + random number + SHA-1 of order ID) of the order operation data generated during offline operation (order ID, order acceptance time, navigation path key points) was generated and compared with the records on the server. After verification, the hashes of the three offline operation data were consistent and there were no conflicts, so they were uploaded to the server in batches; the server returned an "order completion confirmation" receipt (including the final amount of 286 yuan and the user review entry), and the local data synchronization completion rate was 100%. Resource and Cache Updates: The local resource version list (v1.2.5) is consistent with the latest version on the server. However, it was detected that the emergency-contact.png file in the ServiceWorker cache was experiencing read latency due to storage fragmentation (average latency increased from 2ms to 8ms). This triggered the cache repair mechanism, which re-downloaded the file (200KB) and replaced the old version. At the same time, the dynamic cache eviction policy was updated. Due to the recent increase in drivers' nighttime orders to 60%, the usage frequency of dark-mode.css increased from twice a week to five times a week, and the cache priority was upgraded from level two to level one. The subsequent loading speed is expected to improve by 30%.

[0074] In this order, the system provided the driver with a stable, seamless service: the version update took only 47 seconds; the availability of core functions (order acceptance, navigation, and emergency contact) was 100% in offline scenarios; and the degradation prompts for non-core functions were clear. After network recovery, data synchronization was 100% successful, with no order loss or inconsistencies. For the platform, the technical solution brought significant benefits: H5 resource update traffic was reduced by 72% (only 780KB of different resources needed to be downloaded, instead of the full 4.7MB), cache hit rate increased to 91% (the scenario-based hierarchical strategy accurately matches over 90% of users' high-frequency operations), and maintenance costs decreased by 45% (no manual intervention is required for offline resource management; the system automatically completes version verification, cache eviction, and repair).

[0075] From the moment the driver's phone screen lit up with the notification "Order completed, payment received" to his return home after work, this system not only ensured the continuity of individual services but also reflected the core value of H5 offline and incremental resource update technologies in the mobile internet. Through data-driven resource management, intelligent version control, and scenario-based caching strategies, core services remained reliable and the user experience remained smooth, even in scenarios with weak network connectivity. This is not only a successful example of technology implementation but also a profound practice of the "user-centric" product philosophy in the underlying architecture.

[0076] In summary, this application's embodiments, through version differentiation verification, breakpoint resume incremental updates, and scenario-based caching, ensure 100% availability of core functions such as order acceptance, navigation, and emergency contact even in weak network or offline scenarios. Non-core functions are gracefully downgraded with clear prompts, completely resolving the pain point of "service interruption due to network instability," significantly improving service continuity and trust among drivers. For platform operators, automated version management, cache eviction, and repair mechanisms reduce maintenance costs by 45%, enabling dynamic resource optimization without manual intervention. Simultaneously, the collection and analysis of scenario-based feature data feeds back into product iteration, forming a positive cycle of "data-driven - experience optimization - efficiency improvement."

[0077] Figure 10 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 1001, the processor 1002, and the computer program stored on the memory 1001 and capable of running on the processor 1002.

[0078] When the processor 1002 executes the program, it implements the method of H5 offlineization and incremental resource update system of the chauffeur app provided in the above embodiment.

[0079] Furthermore, electronic devices also include: Communication interface 1003 is used for communication between memory 1001 and processor 1002.

[0080] The memory 1001 is used to store computer programs that can run on the processor 1002.

[0081] The memory 1001 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0082] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, then the communication interface 1003, memory 1001, and processor 1002 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 10 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0083] Optionally, in a specific implementation, if the memory 1001, processor 1002, and communication interface 1003 are integrated on a single chip, then the memory 1001, processor 1002, and communication interface 1003 can communicate with each other through an internal interface.

[0084] The processor 1002 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of this application.

[0085] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for the H5 offline implementation and incremental resource update system of a ride-hailing app.

[0086] Furthermore, this application also provides a computer program product, including a computer program or instructions, which, when executed, implement the above-described method for the H5 offline version and incremental resource update system of a ride-hailing app.

[0087] In the description of this specification, the references to "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0088] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0089] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0090] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any of the following techniques known in the art, or a combination thereof: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0091] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0092] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A system for offline H5 functionality and incremental resource updates for a ride-hailing app, characterized in that, include: The module includes a data acquisition module, an incremental update module, an offline cache management module, a network adaptive scheduling module, and a data synchronization module; among them, The data acquisition module is used to collect core H5 page resource data, interface configuration information, and chauffeur scenario characteristic data of the chauffeur app. The incremental update module is used to build a local resource version list and perform differential verification with the server-side resource version list. If there is a version difference, the incremental update process is triggered, and the differential resources are downloaded and verified in blocks through the breakpoint resume engine. The offline cache management module is used to build an offline cache management system through ServiceWorker, cache core H5 resources in a hierarchical manner according to the priority of the ride-hailing scenario, generate an offline available resource pool, and establish a dynamic cache eviction mechanism. The network adaptive scheduling module is used to adaptively schedule resources and activate the full H5 function when online, based on the network status adaptive scheduling strategy. When offline, it enables the offline available resource pool and triggers the mechanism of retaining the core functions of the chauffeur service and degrading the non-core functions. The data synchronization module is used to synchronize the offline chauffeur operation data to the server through a data consistency verification algorithm when the network is restored, and at the same time update the local resource version list and cache status.

2. The H5 offline version and incremental resource update system for a designated driver app according to claim 1, characterized in that, The data acquisition module includes a resource list acquisition unit, an interface configuration acquisition unit, and a scene feature acquisition unit. The resource list acquisition unit periodically obtains a version list of H5 static resources from the server and records the hash value version number of each file. The interface configuration acquisition unit specifically collects the configuration information of API interfaces related to the chauffeur service. The scene feature acquisition unit collects specific environmental data for chauffeur services in real time and dynamically identifies business priorities.

3. The H5 offline version and incremental resource update system for a designated driver app according to claim 1, characterized in that, The incremental update module includes a list acquisition unit, a differential comparison unit, an incremental download unit, and a list synchronization unit. The list acquisition unit actively requests a resource version list from the server and records the hash value version number of each H5 static file. The differential comparison unit accurately identifies newly added, modified, or deleted files by comparing the hash values ​​of the online and local lists, performing file-level change detection. The incremental download unit, based on the differential list, uses a breakpoint resume engine to download only changed resources, supporting recovery from the point of interruption after network interruption. The list synchronization unit atomically updates the local list after downloading.

4. The H5 offline version and incremental resource update system for a designated driver app according to claim 1, characterized in that, The offline cache management module includes a ServiceWorker management unit, a resource hierarchical caching unit, an offline resource pool maintenance unit, and a cache dynamic eviction unit. The ServiceWorker management unit is responsible for registering and activating ServiceWorkers, intercepting resource requests from H5 pages and executing cache priority strategies, while also managing the caching logic for multiple versions of resources. The resource hierarchical caching unit categorizes and caches H5 static resources based on the priority of the ride-hailing scenario. The offline resource pool maintenance unit integrates the effective resources after hierarchical caching, constructs a structured offline available resource pool, and updates the resource status in real time. The cache dynamic eviction unit automatically evicts low-priority resources that have not been used for a long time based on resource usage frequency, cache validity period, and device storage capacity.

5. The H5 offline version and incremental resource update system for a designated driver app according to claim 1, characterized in that, The network adaptive scheduling module includes a network status detection unit, a strategy decision-making unit, and a function scheduling unit. The network status detection unit monitors the network connection status of devices in real time, using a heartbeat mechanism or API response timeout judgment to provide basic data input for adaptive scheduling. The strategy decision-making unit dynamically selects the optimal strategy based on the network status, prioritizing the incremental update of resource synchronization and activating full H5 functionality when online, and automatically switching to a degraded mode when offline. The function scheduling unit specifically executes the strategy decisions, coordinating resource downloads and function activation when online, and activating the offline available resource pool when offline, prioritizing the retention of the core chauffeur service function and degrading non-core functions.

6. The H5 offline version and incremental resource update system for a designated driver app according to claim 1, characterized in that, The data synchronization module includes an offline data collection unit, a data verification and synchronization unit, and a status update unit. The offline data collection unit is responsible for continuously collecting ride-hailing operation data during offline periods and temporarily storing it in a local queue. After the network is restored, the data verification and synchronization unit verifies the integrity of the offline data through a data consistency verification algorithm and then securely transmits it to the server. After successful synchronization, the status update unit atomically updates the local resource version list and cache status, synchronizes server-side changes, and cleans up processed data.

7. A method for implementing an H5 offline system and incremental resource update system for any one of the ride-hailing apps according to claims 1-6, characterized in that, include: Collect core H5 page resource data, interface configuration information, and characteristic data of the designated driver app; Based on the core H5 page resource data, interface configuration information, and chauffeur scenario feature data of the chauffeur app, a local resource version list is constructed and compared with the server-side resource version list for differential verification. If there is a version difference, an incremental update process is triggered. The differential resources are downloaded and verified in blocks through the breakpoint resume engine. At the same time, an offline cache management system is constructed based on ServiceWorker, and the core H5 resources are cached hierarchically according to the priority of the chauffeur scenario, generating an offline available resource pool and establishing a dynamic cache eviction mechanism. According to the aforementioned cache dynamic eviction mechanism, the network status adaptive scheduling strategy is used to synchronously and incrementally update resources and activate the full H5 function when online, and to enable the offline available resource pool when offline, triggering the mechanism of retaining the core functions of the chauffeur service and downgrading non-core functions. When the network is restored, the chauffeur service operation data in the offline state is synchronized to the server through the data consistency verification algorithm, and the local resource version list and cache status are updated at the same time.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method of H5 offline versioning and incremental resource update system for a ride-hailing app as described in claim 7.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed, they implement the method of H5 offlineization and incremental resource update system of a ride-hailing app as described in claim 7.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed, they implement the method of H5 offlineization and incremental resource update system of a ride-hailing app as described in claim 7.

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