Card type fast application deployment method, system and device based on multi-kernel fusion system

Abstract

The invention discloses a card type fast application deployment method, system and device based on a multi-kernel fusion system, and the method comprises the steps: decomposing a fast application into a plurality of card modules with independent functions, determining the dependency relationship between the card modules, and then determining a deployment sequence; configuring and determining a self-adaptive interface and a constraint value of each card module; and determining the adaptation degree of the kernel and each card module according to various indexes, further determining the minimum deployment condition value of each card module and the kernel, performing deployment, and managing the running state of the card module according to the multi-dimensional context data. According to the method, the fast application is decomposed into a plurality of card modules with independent functions, the optimal deployment combination for deploying the card modules on the kernel is determined by comprehensively considering multiple indexes, efficient operation of each kernel and the application module is ensured, and the method can be widely applied to the technical field of fast application deployment.

Description

Technical Field

[0001] The present invention relates to the field of quick application deployment technology, and in particular to a card-type quick application deployment method, system, and device based on a multi-core fusion system. Background Art

[0002] As a new type of application, quick apps have rapidly emerged in the mobile app market, attracting widespread attention for their installation-free, click-and-use features. They combine the convenience of web apps with the high performance of native apps, providing users with a lightweight, responsive experience. However, as quick apps become increasingly complex and their application scenarios continue to expand, they face numerous challenges in development and deployment.

[0003] Traditional application development models often employ a monolithic architecture, integrating all functions into a single application. This model exhibits significant drawbacks in fast application scenarios, making it difficult to meet the rapid iteration and flexible deployment requirements of fast applications. For example, when a quick application needs to add or modify a function, a monolithic architecture may require redevelopment and retesting of the entire application, resulting in low development efficiency and the introduction of new issues.

[0004] Different operating environments (including differences in device hardware, operating system versions, and volatile network environments) pose significant challenges to the stable operation of quick apps. In terms of device hardware, from low-end entry-level devices to high-end flagship devices, their CPU, memory, GPU, and other hardware configurations vary greatly, making it difficult for quick apps to maintain good performance across all devices. In terms of operating systems, different manufacturers have different operating system versions and levels of customization, placing extremely high compatibility requirements on quick apps. In terms of network environments, from high-speed and stable Wi-Fi networks to unstable mobile networks, quick apps need to provide smooth services under various network conditions, which poses a severe test of their network adaptability.

[0005] Multi-core converged systems offer new solutions to the challenges faced by fast applications. By integrating multiple cores with different characteristics and leveraging their strengths, they are expected to improve the performance, compatibility, and scalability of fast applications. For example, if one core focuses on graphics processing while another excels at network communication, the coordinated operation of these cores can enable fast applications to excel in both graphics rendering and network data transmission.

[0006] However, multi-core converged systems present numerous challenges in practical applications. First, there's the issue of kernel and application module compatibility. Because different kernels have varying functions and characteristics, accurately determining the compatibility of each application module with different kernels and achieving the optimal kernel-module combination remains a pressing challenge. Second, in a multi-core environment, system resource management and allocation become increasingly complex. With multiple kernels running simultaneously, it's difficult to rationally allocate resources like CPU, memory, and storage, and resource conflicts and waste are common. Consequently, traditional approaches cannot guarantee the efficient operation of all kernels and application modules. Summary of the Invention

[0007] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0008] To this end, one purpose of an embodiment of the present invention is to provide a card-type quick application deployment method based on a multi-core fusion system, which decomposes the quick application into several functionally independent card modules. Under various complex conditions, multiple indicators are comprehensively considered to determine the optimal deployment combination plan of the card module and the core, ensuring the efficient operation of each core and application module.

[0009] Another object of an embodiment of the present invention is to provide a card-type quick application deployment system based on a multi-core fusion system.

[0010] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include:

[0011] In a first aspect, an embodiment of the present invention provides a card-type quick application deployment method based on a multi-core fusion system, comprising:

[0012] Decompose the quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies;

[0013] Configuring the adaptive interface of each of the card modules and determining the constraint value of the adaptive interface;

[0014] determining the compatibility between the kernel and each of the card modules according to kernel status information, performance requirement coefficients of each of the card modules, and the dependency between the kernel and each of the card modules;

[0015] Determining a minimum deployment status value according to the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information, and kernel deployment environment complexity dimension information;

[0016] Deploy and combine each of the card modules and the core according to the minimum deployment status value;

[0017] The correlation degree is determined according to the multi-dimensional context data information, and the operation state of the card module is managed according to the correlation degree.

[0018] Furthermore, the constraint value of the adaptive interface is calculated using a first mathematical formula, where the first mathematical formula is:

[0019]

[0020] Among them, I constraint is the constraint value, n is the number of functional items associated with the interface, α i is the importance weight coefficient of the i-th function item, F i input is the input data volume of the i-th function item, F i output is the output data volume of the i-th function item, γ i is the environmental adaptation coefficient, is the time dynamic adjustment coefficient, δ i is the network quality impact coefficient, χ i is the interface stability coefficient, λ new is the functional item interaction entropy coefficient, μ dynamix is the interface dynamic load adjustment coefficient, ν con is the context-aware interface adaptation coefficient, the constraint value I constraint ≤preset interface constraint threshold θ.

[0021] Further, the environmental adaptation coefficient γ is calculated by the second mathematical formula i , the second mathematical formula is:

[0022]

[0023] Among them, h is the number of hardware parameter dimensions, λ ij is the sensitivity coefficient of the i-th function item to the j-th hardware parameter, Hj is the actual value of the j-th hardware parameter, μ i is the threshold, κ ε is the hardware parameter interaction coefficient, ξ har is the dynamic interaction coefficient of hardware parameters.

[0024] Furthermore, the kernel status information includes a compatibility coefficient, energy efficiency, security risk coefficient, kernel scalability coefficient, and kernel impact coefficient. The compatibility between the kernel and each of the card modules is calculated using a third mathematical formula. The third mathematical formula is:

[0025]

[0026] Among them, k ijis the degree of adaptation, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, and w1+w2+w3=1, P i com is the compatibility coefficient of the i-th kernel, T j per is the performance requirement coefficient of the jth card module, E i ene is the energy efficiency of the i-th core, D ij dep The dependency of the i-th core and the j-th card module, is the safety risk coefficient, τ ij is the core scalability coefficient, and NH is the core impact coefficient.

[0027] Furthermore, the kernel influence coefficient NH is calculated by a fourth mathematical formula, which is:

[0028] NH=η new ×T synergy

[0029] Among them, η new is the kernel resource dynamic allocation coefficient, T synergy is the inter-core synergy coefficient.

[0030] Further, the compatibility coefficient P is calculated by the fifth mathematical formula: i com , the fifth mathematical formula is:

[0031]

[0032] Among them, N i prol is the number of protocols supported by the i-th kernel, N max-prol N is the maximum number of protocols supported by all kernels. i for N is the number of data formats supported by the i-th kernel. max-for S is the maximum number of data formats supported by all kernels. i cob is the synergy coefficient between the i-th core and other cores, η i is the kernel update frequency influence coefficient, σ i is the core ecological activity coefficient, ρ e-g It is the core ecological growth coefficient.

[0033] Furthermore, the minimum deployment status value is calculated using a sixth mathematical formula, which is:

[0034]

[0035] Among them, O opt is the minimum deployment status value, C i dep is the deployment cost of the i-th kernel, q is the number of system resource limitation dimensions, ξ sj is the influence weight of the sth system resource limitation dimension on the jth card module, L sj is the limit value of the jth card module in the sth system resource limit dimension, r is the number of system service quality indicator dimensions, w tj is the importance weight of the t-th system service quality indicator dimension to the j-th card module, M tj The target value of the jth card module in the tth system service quality indicator dimension, v is the number of kernel deployment environment complexity dimensions, u uj is the influence weight of the u-th environmental complexity dimension on the j-th card module, ν uj is the adaptation value of the j-th card module in the u-th environmental complexity dimension.

[0036] Furthermore, the multi-dimensional context data information includes weights, current values, historical values, and average values ​​corresponding to several data dimensions. The correlation degree is calculated using a seventh mathematical formula, which is:

[0037]

[0038] Among them, R con is the correlation degree σ is the correlation degree adjustment coefficient, ρ k is the weight of the kth data dimension, D k cur is the current value of the kth data dimension, D k his is the historical value of the kth data dimension, D k ave is the average value of the kth data dimension, τk is the data fluctuation impact index, ν k is the data credibility coefficient, w k1 is the data correlation enhancement coefficient, φ new is the context data burstiness coefficient, γ uni is the context-specific coefficient, δ ene is the emergency context response coefficient, π log-ter is the long-term trend context adaptation coefficient.

[0039] In a second aspect, an embodiment of the present invention provides a card-type quick application deployment system based on a multi-core fusion system, including:

[0040] A quick app decomposition module is used to decompose a quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies;

[0041] An adaptive interface configuration module, configured to configure the adaptive interface of each of the card modules and determine the constraint value of the adaptive interface;

[0042] a compatibility determination module, configured to determine the compatibility between the kernel and each of the card modules based on kernel status information, a performance requirement coefficient of each of the card modules, and a dependency between the kernel and each of the card modules;

[0043] A minimum deployment status value module is used to determine a minimum deployment status value according to the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information and kernel deployment environment complexity dimension information;

[0044] A deployment combination module, configured to deploy and combine each of the card modules and the core according to the minimum deployment status value;

[0045] The correlation determination module is used to determine the correlation according to the multi-dimensional context data information, and manage the operation status of the card module according to the correlation.

[0046] In a third aspect, an embodiment of the present invention provides a device, including:

[0047] at least one processor;

[0048] at least one memory for storing at least one program;

[0049] When the at least one program is executed by the at least one processor, the at least one processor implements the above-mentioned card-type quick application deployment method based on a multi-core fusion system.

[0050] The advantages and benefits of the present invention will be described in part in the following description and will become apparent from the following description or learned through practice of the present invention:

[0051] The embodiments of the present invention decompose quick applications into multiple functionally independent card modules, determine the dependencies between the card modules, and then deploy the card modules according to the dependency order, ensuring a clear basis for subsequent deployment order. By ensuring that the adaptive interface constraint value of each card module meets the threshold, the card module can rationally allocate resources according to actual operation requirements, monitor abnormal conditions, and ensure interface stability. By combining the dependency and kernel status information to calculate the compatibility between the kernel and the card module, the compatibility of each kernel for different card modules is intuitively displayed, providing a key basis for subsequent deployment decisions. By integrating the compatibility and multiple constraint information, a comprehensive analysis is performed to obtain the minimum deployment status value, determining the deployment combination of the card module and the kernel that can achieve optimal utilization of system resources and maximize application performance. By processing multi-dimensional context data information collected during operation, the correlation between the context data and the operation status of the quick application is obtained, thereby achieving dynamic management of the operation status of the card module, effectively improving the system's responsiveness to changes in the context environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the steps of a card-type quick application deployment method based on a multi-core fusion system provided by an embodiment of the present invention;

[0053] Figure 2 A schematic diagram of a card-type quick application deployment system based on a multi-core fusion system provided by an embodiment of the present invention;

[0054] Figure 3 A schematic structural diagram of a device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The embodiments of the present invention are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0056] In the description of the present invention, "a plurality" means two or more. The terms "first" and "second" are used solely to distinguish technical features and are not to be construed as indicating or implying relative importance, or as implicitly indicating the number of the indicated technical features, or as implicitly indicating the order of the indicated technical features. Furthermore, unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art.

[0057] Figure 1 A schematic diagram of the steps of a card-type quick application deployment method based on a multi-core fusion system provided by an embodiment of the present invention, referring to Figure 1 The embodiment of the present invention provides a card-type quick application deployment method based on a multi-core fusion system, including:

[0058] S101. Decompose the quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies.

[0059] S102, configuring the adaptive interface of each card module and determining the constraint value of the adaptive interface;

[0060] S103, determining the compatibility between the kernel and each card module based on the kernel status information, the performance requirement coefficient of each card module, and the dependency between the kernel and each card module;

[0061] S104, determining a minimum deployment status value based on the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information, and kernel deployment environment complexity dimension information;

[0062] S105, deploying and combining each card module and the core according to the minimum deployment status value;

[0063] S106 : Determine the correlation degree according to the multi-dimensional context data information, and manage the operating status of the card module according to the correlation degree.

[0064] Specifically, in this embodiment, the overall functionality of a quick app is meticulously broken down into multiple independent card modules based on business logic and functional characteristics. Each module has a clear and single functional responsibility. This design allows developers to focus on the development and implementation of a single module, improving development refinement and code quality.

[0065] To better determine the deployment order of each card module, it's necessary to clarify the inherent connections between them and determine their dependencies. Some modules may serve as foundational support modules, upon which other modules rely for proper operation. Other modules are triggered to run based on specific conditions and rely on data or status provided by other modules. For example, in the aforementioned social quick app, the messaging and chat module may rely on the user profile module to obtain information such as the user's identity. By determining these dependencies, a clear basis for the subsequent deployment order is established.

[0066] After clarifying the dependencies, the system loads modules in sequence. For example, it loads the user login and registration module first, and then loads the functional modules that rely on its authentication. This avoids resource waste and loading confusion, improves the startup and running speed of the application, and reduces mutual interference. If one module fails, it is less likely to trigger a chain reaction that causes the entire application to crash, ensuring stable application operation. For example, a failure in the route planning module in the map quick application does not affect the location positioning module.

[0067] Functionally independent card modules allow different development teams or personnel to work in parallel, designing and matching adaptive interfaces based on the functional characteristics and operational requirements of each card module. These interfaces have intelligent perception and adjustment capabilities and can monitor changes in the operating environment in real time. For example, when it is detected that the device switches from a Wi-Fi network to a mobile data network, the interface can automatically adjust the data transmission strategy, reduce the data transmission frequency, or optimize the data format to adapt to the relatively limited bandwidth resources of the mobile network. At the same time, to ensure that the interface can operate stably, securely, and efficiently under various circumstances, the interface constraint values ​​are strictly set and meet specific conditions. These conditions cover performance indicators (such as the upper limit of response time, the lower limit of data transmission rate), security specifications (such as data encryption requirements, access permission control), and many other aspects.

[0068] Different devices differ in hardware performance (such as different CPU, memory, GPU configurations, etc.), operating system versions, screen resolution, etc. The adaptive interface can automatically detect device characteristics and adjust its own parameters and functions. For example, on low-configuration devices, it reduces graphics rendering accuracy and data transmission volume to ensure smooth operation of the application; on high-resolution screen devices, it optimizes the display effect to present clearer images and text.

[0069] The adaptive interface allows for the rational allocation of resources based on the actual operational needs of the card modules. For example, for computationally intensive card modules (such as image processing and complex algorithm calculations), the interface can coordinate and allocate more CPU resources during runtime to increase processing speed. For modules with frequent data transmission, network resources can be dynamically adjusted to ensure smooth data transmission and improve overall performance.

[0070] The adaptive interface monitors abnormalities during operation, such as memory overflow and program errors, and takes appropriate measures. For example, when excessive memory usage is detected, it automatically clears cached data and optimizes memory allocation to prevent application crashes. When a program error occurs, it attempts to automatically repair it or roll back to a stable state to ensure continuous and stable application operation.

[0071] The purpose of making the constraint values ​​of each card module's adaptive interface meet the conditions is to ensure the stability of the interface. The constraint values ​​limit the resource usage and call frequency of the interface. For example, limiting the number of calls to the interface per unit time can avoid system crashes caused by excessively frequent calls leading to exhaustion of system resources. Specifying the data transmission format, size and other constraints of the interface can ensure accurate data transmission and processing. The constraint value can prompt the interface to optimize internal processing logic and resource allocation. By constraining the interface's use of resources (such as memory, CPU, etc.), the system can reasonably allocate resources to different modules. For example, limiting the memory usage limit of a card module interface can prevent it from excessively occupying memory, ensure that other modules can also obtain sufficient resources, and improve the resource utilization efficiency of the entire quick application.

[0072] Comprehensively collect kernel status information, including kernel type (such as microkernel, macrokernel, etc.), performance parameters (such as CPU operation speed, memory management efficiency), compatibility performance (support for different device hardware and software components), etc.; at the same time, obtain the performance requirement coefficient of the card module, that is, the specific demand degree of each card module for various resources (such as computing resources, storage resources, network resources) during operation.

[0073] The collected information is comprehensively processed and analyzed to produce a value that accurately reflects the compatibility between the core and the card module. This value intuitively demonstrates the compatibility of each core with different card modules, providing a key basis for subsequent deployment decisions. For example, if a card module's compatibility score is found to be high on core A, it means that running on core A will achieve better performance and resource efficiency.

[0074] Obtain the number of system resource limitation dimensions to clarify the system's limitations in each resource dimension, such as CPU usage cannot exceed 80% and memory usage is capped at a certain value. Furthermore, focus on the number of market demand change dimensions to analyze the changing trends in user demand for quick app features and services across different time periods, user groups, and market environments. For example, during holidays, demand for promotional features for e-commerce quick apps increases significantly. Furthermore, collect detailed information on the impact of system resource limitation dimensions on card modules (e.g., which card modules' performance will be severely impacted when CPU resources are tight) and the impact of market demand change dimensions on card modules (e.g., increased market demand for a new feature will prompt the related card modules to require more resources).

[0075] The previously determined compatibility between the core and card modules is deeply integrated with the aforementioned information on system resource limitations and market demand changes. Through continuous iterative calculations and solution evaluation, the minimum deployment state value is ultimately obtained. This value represents the optimal deployment combination after comprehensively considering various complex factors. Specifically, it determines which card modules should be deployed on which cores to achieve optimal utilization of system resources and maximize application performance.

[0076] After determining the deployment combination of the kernel and card modules, during the quick app's operation, the collected multi-dimensional contextual data must be thoroughly processed and analyzed to determine the correlation between this contextual data and the quick app's operating status. Based on this calculated correlation, the card module's operating status is dynamically managed. When the correlation indicates a change in the current context that significantly impacts one or more card modules, the system responds promptly.

[0077] In some optional embodiments, the constraint value of the adaptive interface is calculated using a first mathematical formula, which is:

[0078]

[0079] Among them, I constraint is the constraint value, n is the number of functional items associated with the interface, α i is the importance weight coefficient of the i-th function item, F i input is the input data volume of the i-th function item, F i output is the output data volume of the i-th function item, γ i is the environmental adaptation coefficient, is the time dynamic adjustment coefficient, δ i is the network quality impact coefficient, χ i is the interface stability coefficient, λ new is the functional item interaction entropy coefficient, μdynamix is the interface dynamic load adjustment coefficient, ν con is the context-aware interface adaptation coefficient, and the constraint value I constraint ≤preset interface constraint threshold θ.

[0080] Specifically, in this embodiment, by determining the number of function items n associated with the interface, the importance weight coefficient α of each function item is determined. i This coefficient reflects the criticality of the function item in the entire interface function system. It is determined by the fuzzy hierarchical analysis method combined with historical data statistics, and dynamically changes with the actual situation using the dynamic weight adjustment algorithm based on reinforcement learning. At the same time, the input data volume F of the i-th function item is obtained i input and the output data volume F i output The ratio of the two reflects the data conversion characteristics of the functional item.

[0081] By introducing the environmental adaptation coefficient γ i , combined with the hardware parameters of the running device (such as CPU main frequency, memory capacity, etc.), it can reflect the adaptability of the interface to different hardware environments; the time dynamic adjustment coefficient φ i The interface can reasonably adjust its operation strategy under different time conditions based on the changing patterns of user usage habits such as different time periods within a day, seasonal changes, special event calendars, etc.

[0082] Network quality impact coefficient δ i Related to the current network conditions such as network bandwidth, delay, and packet loss rate, it reflects the impact of the network environment on the interface in real time; user personalized preference coefficient ψ i It is derived from user historical operation data, user portraits, etc. to meet user personalized needs.

[0083] Interface stability coefficient χ i The stability of the interface is measured based on the fluctuation of the interface's historical operation data; the functional item interaction entropy coefficient λ new It is obtained by calculating the information interaction entropy between the function item and other related function items, reflecting the complexity of information interaction between function items. Interface dynamic load adjustment coefficient μ dynamix According to the real-time monitoring of the interface request load, the interface performance is adjusted under different loads; the context-aware interface adaptation coefficient ν con Specifically, it is the degree of match between the current context (including user location, device status, time, etc.) and the interface function requirements. Finally, these factors are substituted into the formula

[0084]

[0085] Calculate the constraint value I constraint .

[0086] After determining the constraint value, the calculated constraint value I constraint Compare with the preset interface constraint threshold θ for judgment and regulation. constraint ≤ the preset interface constraint threshold θ, indicating that the current operating state of the interface meets the pre-set conditions and can continue to operate normally. constraint If the interface constraint exceeds the preset interface constraint threshold θ, it indicates that the interface is experiencing issues such as excessive resource usage and substandard performance. Based on the information provided by each coefficient, the system determines which factor or factors are causing the constraint value to exceed the threshold and then adjusts the interface operating parameters and resource allocation strategy accordingly. For example, if the network quality coefficient is found to be causing the constraint value to be too high, the system can adjust the data transmission strategy to reduce reliance on the network, bringing the interface constraint value back within a reasonable range and ensuring stable and efficient operation of fast applications.

[0087] By calculating constraints based on multiple factors, including functional importance, data input and output, environment, and time, the card module's adaptive interface can precisely adapt to complex and changing operating conditions. This ensures that the interface allocates resources appropriately in different scenarios, achieving efficient operation and avoiding performance bottlenecks caused by inappropriate resource allocation. For example, in the event of poor network conditions, the network quality impact factor and the interface's dynamic load adjustment factor are used to reduce data transmission volume and processing complexity, ensuring the normal operation of core functions.

[0088] Constraining interfaces using factors such as interface stability coefficients effectively monitors and regulates their operational status, reduces the probability of interface errors, and enhances the stability and reliability of the entire quick application system. When abnormal fluctuations occur in an interface, constraints based on the correlation coefficient enable timely adjustments, preventing application crashes caused by interface failures.

[0089] By leveraging dynamic time adjustment coefficients and interface load adjustment coefficients, interfaces can flexibly respond to dynamic conditions such as system load changes, network fluctuations, and environmental changes. During peak system loads, interface resource usage and processing strategies are automatically adjusted to ensure overall application performance is not affected, improving the environmental adaptability of fast applications.

[0090] In some optional embodiments, the environment adaptation coefficient γ is calculated by a second mathematical formula i , the second mathematical formula is:

[0091]

[0092] Among them, h is the number of hardware parameter dimensions, λ ij is the sensitivity coefficient of the i-th function item to the j-th hardware parameter, Hj is the actual value of the j-th hardware parameter, μ i is the threshold, κ εis the hardware parameter interaction coefficient, ξ har is the dynamic interaction coefficient of hardware parameters.

[0093] Specifically, in this embodiment, the number of hardware parameter dimensions h is determined, covering multiple dimensions such as the device's CPU main frequency, memory capacity, GPU model, etc. For each function item i, its sensitivity to different hardware parameters j is determined to obtain the sensitivity coefficient λ ij For example, the graphics rendering function is sensitive to GPU performance, and its corresponding λ ij The value is relatively high; while the text processing function is less sensitive to the GPU. At the same time, the actual value Hj of each hardware parameter j is obtained, such as the current device's CPU main frequency value, remaining memory capacity, etc.

[0094] Set the threshold μ i Used to measure the acceptable range of hardware parameters for functional items. Introducing the hardware parameter interaction coefficient ξ har , which reflects the comprehensive impact of different hardware parameters working together on functional items. For example, the impact of CPU and memory working together on application data processing speed is reflected by κ ε Quantify. There is also the hardware parameter dynamic interaction coefficient ξ har , which is used to reflect the interactive changes of hardware parameters under dynamic conditions such as different loads and different running times.

[0095] Substituting the above parameters into the formula

[0096]

[0097] Calculate the exponential part first It comprehensively reflects the weighted influence of the function item on the deviation between the actual value of the hardware parameter and the threshold value and the sensitivity coefficient. Through the exponential operation and fractional operation of e, a basic coefficient based on the deviation of the hardware parameter is obtained, and then multiplied by κ ε and ξ har , comprehensively considering the interactive effects of hardware parameters and dynamic change factors, and finally obtaining the environmental adaptation coefficient γ i This coefficient is used to measure the adaptability of the interface to the functional items in the current hardware environment, providing an important basis for adjusting the interface's operation strategy and rationally allocating resources to ensure that the quick application runs stably and efficiently in this hardware environment.

[0098] By accurately calculating the environmental adaptation coefficient, the card module's adaptive interface can better adapt to different hardware environments. Regardless of changes in device hardware parameters, the interface can appropriately adjust its operation strategy based on this coefficient, ensuring stable and efficient operation of quick applications on various devices. For example, on low-configuration devices, the interface can automatically reduce resource consumption based on the environmental adaptation coefficient to ensure smooth application operation.

[0099] Considering the interactions between hardware parameters and the sensitivity of functional items to these parameters helps the system allocate resources more rationally. Based on the environmental adaptation coefficient, resources are precisely allocated to functional items that best match the hardware parameter requirements, improving resource utilization efficiency and avoiding resource waste.

[0100] This calculation method comprehensively considers hardware parameters, making quick apps compatible with a wider range of devices. Whether it's a new high-end device or an older one, the environmental adaptation coefficient adjusts interface behavior, reducing compatibility issues caused by hardware differences and expanding the applicability of quick apps.

[0101] Real-time calculation of the environmental adaptation coefficient based on dynamic changes in hardware parameters enables the interface to promptly respond to hardware environment changes and dynamically adjust its own functions and performance. When the device hardware status fluctuates, such as when the CPU frequency is reduced due to temperature rise, the interface can adjust the processing logic based on the coefficient to maintain stable application performance and improve the user experience.

[0102] In some optional embodiments, the kernel status information includes a compatibility coefficient, energy efficiency, security risk coefficient, kernel scalability coefficient, and kernel impact coefficient. The compatibility between the kernel and each card module is calculated using a third mathematical formula. The third mathematical formula is:

[0103]

[0104] Among them, k ij is the degree of adaptation, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, and w1+w2+w3=1, P i com is the compatibility coefficient of the i-th kernel, T j per is the performance requirement coefficient of the jth card module, E i ene is the energy efficiency of the i-th core, D ij dep The dependency of the i-th core and the j-th card module, is the safety risk coefficient, τ ij is the core scalability coefficient, and NH is the core impact coefficient.

[0105] In this embodiment, w1, w2, and w3 are weight coefficients, where w1 + w2 + w3 = 1. They are used to measure the relative importance of different factors when calculating the fitness. Their values ​​can be determined based on the specific needs and application scenarios of the quick app. For example, in financial quick apps with extremely high security requirements, the weight associated with the security risk coefficient may be appropriately increased.

[0106] Compatibility coefficient P icom is the compatibility coefficient of the i-th kernel, reflecting the kernel’s ability to support different hardware and software components. For example, if a kernel supports multiple device drivers and file formats, its compatibility coefficient is high and it can better adapt to diverse operating environments.

[0107] Performance requirement coefficient T j per is the performance requirement coefficient of the jth card module, which is determined based on the functional characteristics of the card module. For example, a video playback card module has high requirements for GPU performance and decoding speed, and its performance requirement coefficient will be quantified based on these requirements.

[0108] Energy efficiency E i ene is the energy efficiency of the i-th core, which measures the amount of work completed per unit of energy consumed by the core during operation. Cores with high energy efficiency are more energy-efficient while meeting the performance requirements of the card module and are suitable for power-sensitive devices.

[0109] Dependence D ij dep The dependency between the i-th core and the j-th card module reflects the closeness of their combined operation. A high dependency indicates that the card module has a strong dependence on the specific functions or characteristics of the core, and the security risk factor should be considered when calculating the adaptability. This value is based on factors such as the number of kernel vulnerabilities, the frequency of security patch updates, and the severity of the security vulnerabilities. A lower value indicates a more secure kernel. In applications with high security requirements, a kernel with a lower security risk factor is more compatible with the card module.

[0110] Scalability coefficient τ ij This reflects the kernel's ability to scale when adding new features and responding to load changes. A highly scalable kernel can better adapt to future feature upgrades and business expansion needs of quick apps.

[0111] The kernel influence coefficient NH comprehensively considers kernel factors and quantifies the influence of the kernel in the entire system. Kernels with high influence coefficients will be reflected accordingly in the fitness calculation.

[0112] Substituting the above parameters into the formula

[0113]

[0114] First calculate the products of each item separately: w1×P i com Reflects the contribution of compatibility factors to the adaptability; w2×T j per Reflects the card module performance requirements and kernel adaptation; w3×E i eneIndicates the impact of energy efficiency factors.

[0115] for This part is calculated based on the dependency between the kernel and the card module. The higher the dependency, the closer the value is to 1, and the greater the impact on the adaptability. Then multiply it by the security risk factor. Scalability coefficient τ ij and the kernel impact coefficient NH, which comprehensively considers factors such as security, scalability, and kernel influence.

[0116] Finally, add the product results to get the degree of fit k between the kernel and the card module. ij By calculating the adaptability of different core and card module combinations, we can comprehensively evaluate the adaptability between them and provide a key basis for determining the optimal deployment plan in the future.

[0117] By comprehensively considering multiple factors, such as kernel compatibility, card module performance requirements, and energy efficiency, we can accurately identify the most suitable kernel for each card module. This allows the card module to fully leverage its performance advantages when running on that kernel, avoiding performance bottlenecks caused by inappropriate kernel selection and improving the overall efficiency of fast applications. For example, a compute-intensive card module can be matched with a kernel with high computing performance and reasonable energy efficiency, ensuring fast and stable operation.

[0118] The inclusion of a security risk factor fully considers kernel security when calculating adaptability, mitigating security risks arising from kernel security vulnerabilities and ensuring quick application data security and system stability. Furthermore, the kernel scalability factor is incorporated to ensure the selected kernel can easily adapt to future functional expansion and changing requirements, maintaining long-term system stability.

[0119] Taking into account factors such as core energy efficiency and dependency, it can rationally allocate core resources to avoid resource waste. While meeting the performance requirements of the card module, it reduces energy consumption and improves resource utilization. This is especially suitable for resource-constrained devices, extending device battery life and service life.

[0120] Comprehensively evaluate all aspects of the kernel and card modules to better adapt the system to different hardware environments and changing functional requirements. Whether on high-performance or low-configuration devices, deployment can be flexibly adjusted based on adaptability, ensuring stable and efficient operation of quick applications in various scenarios.

[0121] In some optional embodiments, the kernel influence coefficient NH is calculated by a fourth mathematical formula, which is:

[0122] NH=η new ×T synergy

[0123] Among them, η newis the kernel resource dynamic allocation coefficient, T synergy is the inter-core synergy coefficient.

[0124] Specifically, in this embodiment, the kernel resource dynamic allocation coefficient η new This is primarily determined by analyzing the kernel's resource allocation strategies and their effectiveness under varying load conditions. For example, we observe whether the kernel can quickly and appropriately allocate resources like CPU and memory to key card modules when the system is under high load; and whether it can effectively reclaim idle resources to avoid waste during periods of low load. By quantitatively evaluating these resource allocation behaviors, we derive a coefficient reflecting the kernel's dynamic resource allocation capability.

[0125] Determine the inter-core synergy coefficient: inter-core synergy coefficient T synergy This is achieved by monitoring performance changes when multiple cores work together. For example, when multiple cores work together on a large computing task, the speed, energy consumption, and accuracy of results are compared between single-core and multi-core collaborative processing. The performance improvement ratio brought about by collaborative work is calculated, which in turn determines the inter-core synergy coefficient and measures the effectiveness of inter-core collaboration.

[0126] Calculate the kernel impact coefficient: dynamically allocate the kernel resources to the determined kernel impact coefficient η new and the inter-core synergy coefficient T synergy Substitute into the formula NH = η new ×T synergy , the kernel impact coefficient NH is calculated. This coefficient comprehensively reflects the kernel's capabilities in dynamic resource allocation and collaborative work. It is used to reflect the impact of these two characteristics on the overall compatibility when calculating the kernel and card module compatibility. Ultimately, this coefficient serves as an important reference when determining the deployment combination of the kernel and card modules, enabling the system to select the optimal kernel and improve the operating efficiency and performance of quick applications.

[0127] The kernel resource dynamic allocation coefficient reflects the kernel's ability to flexibly allocate resources under varying load conditions. By incorporating this coefficient into the kernel impact coefficient calculation, the system prioritizes kernels with flexible resource allocation, avoiding resource waste and improving overall resource utilization efficiency. For example, as system load fluctuates, resources can be promptly allocated to high-demand card modules, ensuring efficient application operation.

[0128] The inter-core synergy coefficient reflects the performance improvement brought about by core collaboration. Including this factor in the calculation of the core impact coefficient encourages the system to favor core combinations with good synergy, enhancing inter-core collaboration and improving the overall performance of fast applications in a multi-core environment. For example, when multiple cores work together to handle complex tasks, processing speed and stability can be significantly improved.

[0129] Comprehensive consideration of dynamic resource allocation and core synergy improves efficiency, enabling the system to more rationally evaluate cores and select the most suitable core to host card modules. This helps optimize the performance of quick apps, reduces lags and crashes caused by improper core resource allocation or poor synergy, and enhances system stability.

[0130] During the operation of quick apps, system loads and functional requirements constantly change. Calculating the kernel impact coefficient based on these two coefficients allows the system to promptly adjust the deployment of the kernel and card modules based on the kernel's dynamic performance in resource allocation and coordination, better adapting to various changes and ensuring the continued stable operation of the app.

[0131] In some optional embodiments, the compatibility coefficient P is calculated by the fifth mathematical formula i com , the fifth mathematical formula is:

[0132]

[0133] Among them, N i prol is the number of protocols supported by the i-th kernel, N max-prol N is the maximum number of protocols supported by all kernels. i for N is the number of data formats supported by the i-th kernel. max-for S is the maximum number of data formats supported by all kernels. i cob is the synergy coefficient between the i-th core and other cores, η i is the kernel update frequency influence coefficient, σ i is the core ecological activity coefficient, ρ e-g It is the core ecological growth coefficient.

[0134] In this embodiment, the number of protocols N supported by the i-th kernel is counted. i prol And the maximum number of supported protocols N in all kernels max-prol , by calculating the ratio of the two The relative index of the kernel in terms of protocol support is obtained. The ratio is multiplied by 0.4 to highlight the importance of protocol support in compatibility evaluation.

[0135] Similarly, count the number of data formats supported by the i-th kernel N i for And the maximum number of data formats supported by all kernels N max-for , calculate the ratio And multiply by 0.3 to measure the kernel's ability in supporting data formats.

[0136] Evaluate kernel synergy and ecological parameters to determine the synergy coefficient S between the i-th kernel and other kernels i cob This coefficient is obtained by evaluating the performance of the core in sharing resources and coordinating tasks with other cores in a multi-core environment. The cooperative ability coefficient is multiplied by 0.3 and then combined with the core update frequency influence coefficient η i , core ecological activity coefficient σ i , core ecological growth coefficient ρ e-g Multiply them together to comprehensively consider the development and collaboration of the kernel in the ecosystem. Among them, the kernel update frequency influence coefficient η i Determined by the frequency of kernel release updates and the importance of the updated content; the kernel ecosystem activity coefficient σ i The kernel ecosystem growth coefficient ρ is derived from indicators such as the number of developers in the kernel community, the amount of contributed code, and the activity of technical exchanges. e-g Calculated based on the resource growth and application number growth of the kernel ecosystem over a certain period of time.

[0137]

[0138] Calculate the compatibility coefficient P of the i-th kernel i com This coefficient comprehensively reflects the kernel's compatibility level in terms of protocol support, data format processing, collaborative work, and ecological development. It provides an important basis for subsequent calculation of the adaptability of the kernel and card module, and assists in deciding which kernel is more suitable for carrying a specific card module to ensure the stable operation and good performance of fast applications.

[0139] By calculating the compatibility coefficient based on factors such as the number of protocols supported by the kernel and the types of data formats, we can comprehensively assess kernel compatibility. This helps select a highly compatible kernel, enabling quick apps to run stably on diverse hardware devices, network environments, and software platforms, reducing application failures caused by compatibility issues and expanding the applicability of quick apps.

[0140] Including the core's interoperability coefficient with other cores in the formula encourages the selection of cores with good interoperability. When cores work well together, the efficiency of the entire multi-core fusion system is improved, preventing conflicts and resource waste between cores, making the system more efficient when handling complex tasks.

[0141] Factoring factors like kernel update frequency, ecosystem activity, and ecosystem growth can guide the system toward kernels with a positive development trend. These kernels are typically continuously updated, with bug fixes and new features. Their active ecosystems also provide more technical support and resources, facilitating the long-term maintenance and functionality expansion of quick apps.

[0142] This formula provides a quantitative and comprehensive evaluation standard that weights multiple factors related to kernel compatibility. This allows developers or systems to make more scientific and reasonable decisions based on objective data when selecting a kernel, thereby improving the overall compatibility between the kernel and quick applications.

[0143] In some optional embodiments, the minimum deployment status value is calculated by a sixth mathematical formula, which is:

[0144]

[0145] Among them, O opt is the minimum deployment status value, C i dep is the deployment cost of the i-th kernel, q is the number of system resource limitation dimensions, ξ sj is the influence weight of the sth system resource limitation dimension on the jth card module, L sj is the limit value of the jth card module in the sth system resource limit dimension, r is the number of system service quality indicator dimensions, w tj is the importance weight of the t-th system service quality indicator dimension to the j-th card module, M tj The target value of the jth card module in the tth system service quality indicator dimension, v is the number of kernel deployment environment complexity dimensions, u uj is the influence weight of the u-th environmental complexity dimension on the j-th card module, ν uj is the adaptation value of the j-th card module in the u-th environmental complexity dimension.

[0146] Specifically, in this embodiment, k ij The compatibility between the i-th core and the j-th card module reflects the degree of fit between the core and the card module in terms of function and performance. The higher the compatibility, the more suitable the core is for carrying this card module.

[0147] C i dep is the deployment cost of the i-th kernel, which includes hardware costs, software licensing costs, maintenance costs, etc. The deployment cost of different kernels can vary significantly and is an important economic factor to consider in deployment decisions.

[0148] q is the number of system resource limitation dimensions. For example, CPU usage, memory capacity, network bandwidth, etc. can all be used as resource limitation dimensions.

[0149] ξ sj It is the influence weight of the sth resource constraint dimension on the jth card module, reflecting the importance of the resource constraint dimension to the operation of the card module.

[0150] L sj It is the limit value of the jth card module in the sth resource limit dimension, which clarifies the upper limit of resources that the card module can use in this resource dimension.

[0151] r is the number of system service quality indicator dimensions, such as response time, throughput, error rate, etc., which are all service quality indicator dimensions.

[0152] w tj is the importance weight of the t-th system service quality indicator dimension to the j-th card module, reflecting the criticality of the service quality indicator to the card module;

[0153] M tj It is the target value of the j card module in the tth system service quality indicator dimension, that is, the expected service quality level.

[0154] v is the number of complexity dimensions of the kernel deployment environment, such as the network topology of the deployment environment and the diversity of hardware devices, which can constitute complexity dimensions.

[0155] u uj is the influence weight of the u-th environmental complexity dimension on the j-th card module, indicating the degree of influence of the environmental complexity dimension on the operation of the card module;

[0156] v uj is the adaptation value of the jth card module in the uth environmental complexity dimension, reflecting the adaptability of the card module under this environmental complexity dimension.

[0157] For each possible card module and core deployment combination, substitute the above parameters into the formula

[0158] Calculate the minimum deployment status value O opt .

[0159] First calculate This part comprehensively considers the compatibility of different kernels and card modules as well as the kernel deployment cost, and evaluates the comprehensive cost of deploying card modules on different kernels.

[0160] Then calculate It reflects the impact of system resource limitation on the card module and measures the operating cost of the card module under resource constraints.

[0161] Then calculate Reflects the requirements of the system service quality indicator dimension on the card module and evaluates the cost of meeting the service quality goals.

[0162] Final calculation It represents the impact of the complexity dimension of the kernel deployment environment on the card module and measures the adaptation cost of the card module in a complex environment.

[0163] Add the above results to get the O corresponding to each deployment combination. opt By comparing the O opt values, find the minimum value among them. The deployment combination corresponding to the minimum value is the optimal deployment solution after comprehensive consideration of various factors. That is, the deployment combination of card modules and cores determined by the minimum deployment status value can achieve balanced optimization in multiple aspects such as resources, performance, and cost.

[0164] By comprehensively considering factors such as the compatibility between the core and card modules, system resource limitations, and market demand fluctuations, we can comprehensively evaluate resource utilization under various deployment scenarios. By calculating the minimum deployment status value, we can find the solution that most rationally allocates resources across different dimensions, avoiding excessive or idle resources, improving overall resource utilization efficiency, and reducing operating costs.

[0165] Incorporating system service quality indicators into calculations ensures that key service quality factors such as application performance, response time, and reliability are fully considered during the deployment process. Deployment plans determined based on minimum deployment status values ​​can better meet the card module's service quality goals, provide users with a more stable and efficient experience, and enhance the application's competitiveness.

[0166] By considering the impact of system resource limitations and market demand fluctuations on card modules, quick apps can flexibly adjust their deployment strategies based on changes in the actual operating environment (such as dynamic changes in hardware resources and real-time fluctuations in market demand). This approach finds the optimal deployment solution for different environmental conditions, ensuring stable operation of quick apps in a variety of complex situations and improving their environmental adaptability and risk resistance.

[0167] By combining the core deployment costs and the impact of various factors on card modules, we can balance costs and benefits while meeting application functionality and performance requirements. By minimizing deployment status values, we avoid excessive costs caused by unreasonable deployment choices, achieving maximum application benefits at the lowest cost, and improving the economic feasibility and sustainable development of fast applications.

[0168] In some optional embodiments, the multi-dimensional contextual data information includes weights, current values, historical values, and average values ​​corresponding to several data dimensions, and the correlation is calculated using the seventh mathematical formula, which is:

[0169]

[0170] Among them, R conis the correlation degree σ is the correlation degree adjustment coefficient, ρ k is the weight of the kth data dimension, D k cur is the current value of the kth data dimension, D k his is the historical value of the kth data dimension, D k ave is the average value of the kth data dimension, τk is the data fluctuation impact index, ν k is the data credibility coefficient, w k1 is the data correlation enhancement coefficient, φ new is the context data burstiness coefficient, γ uni is the context-specific coefficient, δ ene is the emergency context response coefficient, π log-ter is the long-term trend context adaptation coefficient.

[0171] Specifically, we first comprehensively collect multi-dimensional contextual data, covering user behavior, device hardware status, network environment, geographic location, time, and other dimensions. We then preprocess the collected data, including data cleaning (removing noise and outliers) and normalization (scaling the data), to ensure data accuracy and consistency, providing a reliable data foundation for subsequent calculations.

[0172] The correlation adjustment coefficient σ is used to make overall adjustments to the final calculated correlation. The correlation calculation result can be optimized based on the specific needs of the quick application and the characteristics of the operating environment.

[0173] Data dimension weight ρ k It reflects the importance of the kth data dimension in the entire context information and highlights the impact of key data dimensions on the association calculation.

[0174] D k cur is the value of the current k-th data dimension, D k his is the historical value of the kth data dimension, D k ave is the average value of the kth data dimension. By comparing the current value with the historical value and the average value, the change of the data dimension is measured.

[0175] The data fluctuation impact index τk is used to quantify the impact of the fluctuation of the kth data dimension on the correlation degree. It is determined through statistical analysis and machine learning algorithms based on the historical fluctuations and current trend of the data dimension.

[0176] Data credibility coefficient ν kEvaluate the credibility of the kth data dimension. This is calculated using a trust assessment model based on factors such as the reliability of the data collection equipment, verification results during data transmission, and blockchain evidence verification.

[0177] Data correlation enhancement coefficient w k1 For the potential correlation between different data dimensions, the degree of correlation between the kth data dimension and other dimensions is determined, thereby obtaining the data correlation enhancement coefficient, which is used to enhance the influence of related data dimensions on the correlation degree.

[0178] Context data burstiness coefficient By performing mutation detection on the data time series, the sudden change of the k-th data dimension data is determined, and the context data suddenness coefficient is calculated to measure the impact of the suddenness of the data on the correlation.

[0179] Context-specific coefficient γ uni Feature extraction is performed on the specific scene in which the user is located (such as office scene, sports scene, leisure scene) to determine the specificity of the context data in the current scene, and the context scene specificity coefficient is obtained to reflect the unique impact of context data in different scenes.

[0180] Emergency context response coefficient δ ene To consider the impact of emergencies (such as equipment failure, network interruption, and security threats) on contextual data, the emergency context response coefficient is calculated by combining historical emergency processing data and real-time monitoring information. This is used to quickly adjust the correlation calculation results in emergency situations and guide applications to take emergency measures.

[0181] Long-term trend context adaptation coefficient π log-ter : Use the time series prediction model to predict the long-term change trend of context data, and calculate the long-term trend context adaptation coefficient based on the prediction results, so that quick applications can adapt to long-term context changes.

[0182] Substitute the above parameters into the formula

[0183] Calculate the correlation R con .

[0184] This correlation value quantifies the degree of correlation between the current context and the running status of the quick application, providing a key basis for the quick application to make intelligent decisions and dynamic management based on context information.

[0185] By integrating multi-dimensional contextual data and calculating correlations based on various influencing factors, the system can accurately capture subtle changes in the context. Whether it's changes in user behavior, fluctuations in device status, or sudden external environmental events, the system can promptly detect and quantify their correlation with the quick app's operating status, providing an accurate basis for intelligent quick app decision-making.

[0186] Optimizing application resource allocation: Based on accurate correlation calculations, quick apps can rationally allocate resources based on the actual needs of the current context. For example, when detecting a user entering a high-load scenario (such as a multi-person video conference) and the correlation indicates a surge in demand for related functions, the system can quickly prioritize resources for key card modules such as video processing and audio transmission, ensuring smooth application operation and avoiding resource waste and inappropriate allocation.

[0187] Enhanced user personalization: By factoring in factors such as user preferences and context-specificity, quick apps can deeply understand individual differences and the specificities of their scenarios. Based on the needs of different users in different scenarios, they dynamically adjust app functionality and interaction methods, providing a highly personalized service experience and enhancing user satisfaction and loyalty to quick apps.

[0188] Enhanced application emergency response capabilities: The introduction of an emergency context response factor enables quick applications to quickly identify and assess the impact of emergencies (such as device failures and network outages) on application operations. Based on correlation calculations, appropriate emergency measures are quickly implemented, such as switching to a backup kernel and disabling non-critical functional modules. This ensures the security of basic application functions and data in emergencies, improving application stability and reliability.

[0189] Adapting to long-term trend changes: Using the long-term trend context adaptation coefficient, quick apps can analyze and track the long-term trend of context data. For example, based on the gradual evolution of user usage habits and the long-term dynamic changes in market demand, quick apps can plan and adjust application functions and resource allocation strategies in advance, enabling them to continuously adapt to environmental changes and maintain competitiveness and good operating status.

[0190] By continuously monitoring the correlation R con A numerical value. A higher correlation indicates a closer connection between the current context and the quick app's running state, suggesting a significant change. For example, in an office scenario, if a user frequently opens a file editing card, the correlation will increase based on changes in user behavior.

[0191] Based on the change in relevance, the system determines which card modules are most closely associated with the current context. For example, in the aforementioned office scenario, card modules such as file editing and document storage are identified as key modules. The system then assesses the resource requirements of these modules based on their performance requirements (e.g., the file editing module's requirements for CPU processing power and memory read / write speed).

[0192] Based on the evaluation results, resources are allocated to key, highly relevant card modules. For example, more CPU resources are allocated to speed up file editing responses, and more memory space is added to improve document storage and access efficiency. At the same time, resources are appropriately reduced for less relevant and currently non-critical card modules to avoid wasted resources. For example, cache space in the image browsing module is temporarily reduced.

[0193] The correlation calculation takes into account the context-specific coefficient γ uni , able to identify the user's current scene. When changes in correlation reflect a scene switch, such as switching from an office setting to a leisure and entertainment setting, the system determines the card module functions that need to be switched or optimized based on pre-set scene-function mappings. For example, in the leisure and entertainment setting, card modules such as video playback and music listening become the focus.

[0194] For each key card module, the system switches to the corresponding function mode. For example, in the video playback module, the system switches from the low-quality, data-saving mode used in office scenarios to the high-definition, smooth playback mode used in leisure scenarios. In the music listening module, the system recommends personalized playlists based on the user's likely more relaxed state in leisure scenarios.

[0195] Using other factors in the correlation calculation, such as the data correlation enhancement coefficient w k1 , context data burstiness coefficient If the user is detected to be switching music at a high frequency in a leisure scenario (data burst), the music listening module will optimize the recommendation algorithm based on the correlation calculation results to more accurately push music that suits the user's current preferences.

[0196] Changes in correlation may indicate a change in the compatibility between the current kernel and the card module. For example, when a user switches from a stable indoor network environment to an outdoor mobile network environment (changes in network dimension data lead to changes in correlation), the compatibility of some card modules with a high dependency on the network with the current kernel may decrease. The compatibility between the kernel and the card module is assessed by reanalyzing kernel status information (such as network processing capabilities), the performance requirement coefficient of the card module, and its dependency.

[0197] Based on the evaluation results, if the current kernel is no longer optimal, the system decides whether to adjust the kernel deployed on the card module. For example, in the aforementioned network environment change scenario, a card module with high network requirements will be migrated to a kernel more capable of handling mobile network environments. The decision-making process also considers factors such as deployment cost and system resource limitations, and the optimal solution is determined through comprehensive calculations (such as using the sixth calculation formula).

[0198] Perform kernel adjustments: After determining the adjustment strategy, the system performs kernel adjustments. During the migration of card modules to the new kernel, this ensures smooth data migration and seamless function switching, avoiding noticeable interruptions or errors to users and ensuring stable operation of quick applications.

[0199] In the application scenario of emergency response, the emergency context response coefficient δ in the correlation calculation is ene , enabling the system to quickly identify emergency situations. When an emergency situation such as low device battery or sudden network interruption is detected, resulting in an abnormal increase in correlation, the system will quickly take countermeasures. For example, in the case of low battery, non-essential card modules (such as animation display modules) are turned off to reduce power consumption; in the case of network interruption, offline card modules are switched to (such as local file viewing modules) and network-dependent functions (such as online video playback) are suspended.

[0200] Long-term trend context adaptation coefficient π log-ter This helps the system analyze long-term trends in contextual data. When changes in correlation reflect long-term trends, such as a gradual shift in user habits from frequent image editing to video editing, the system proactively plans and adjusts resource allocation and feature development priorities for the card module. Resources are increased for the video editing card module, and functional optimization and upgrades are performed to adapt to these long-term changes and maintain the competitiveness and user satisfaction of the quick app.

[0201] It can be appreciated that the embodiments of the present invention offer a solution to the highly coupled code structure of traditional application development by breaking down quick apps into multiple, functionally independent card modules. Different development teams or individuals can simultaneously focus on their respective card modules, without interfering with each other's development work. Determining the dependencies between card modules and deploying them sequentially is like drawing a precise blueprint for the entire application. The system clearly knows which foundational modules to load first, followed by which functional modules that depend on them. Furthermore, the system considers the number of system resource constraints (such as CPU usage limits, memory capacity limits, and network bandwidth constraints), the number of market demand fluctuations (such as differences in user demand for quick app functionality across seasons and time periods), and the impact of these factors on card modules. This information is combined with the compatibility of the kernel and card modules for comprehensive analysis to determine the minimum deployment state. This value represents the optimal deployment combination of card modules and the kernel under various complex constraints.

[0202] At the same time, the embodiment of the present invention obtains multi-dimensional contextual data information (including the user's operating habits, current geographical location, device power and hardware status, etc.), and processes and analyzes it to obtain the correlation. This correlation is like an "environmental perception radar" for quick applications, which can keenly capture the connection between environmental changes and the application's operating status. Based on this correlation, the operating status of the card module is dynamically managed. When it is detected that the user enters a new geographical location and the correlation analysis shows that the location is related to specific service needs, the quick application can quickly adjust the operation of the relevant card module.

[0203] Reference Figure 2 The embodiment of the present invention provides a card-type quick application deployment system based on a multi-core fusion system, including:

[0204] The quick app decomposition module is used to decompose the quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies;

[0205] Adaptive interface configuration module, used to configure the adaptive interface of each card module and determine the constraint value of the adaptive interface;

[0206] A compatibility determination module, configured to determine the compatibility between the kernel and each card module based on kernel status information, performance requirement coefficients of each card module, and the dependency between the kernel and each card module;

[0207] A minimum deployment status value module is used to determine the minimum deployment status value based on the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information, and kernel deployment environment complexity dimension information;

[0208] A deployment combination module is used to deploy and combine each card module and the core according to the minimum deployment status value;

[0209] The correlation determination module is used to determine the correlation based on the multi-dimensional context data information and manage the operation status of the card module according to the correlation.

[0210] The contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0211] Reference Figure 3 , an embodiment of the present invention provides a device, including:

[0212] at least one processor;

[0213] at least one memory for storing at least one program;

[0214] When the at least one program is executed by the at least one processor, the at least one processor implements the card-type quick application deployment method based on a multi-core fusion system.

[0215] An embodiment of the present invention further provides a computer-readable storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to execute the above-mentioned card-type quick application deployment method based on a multi-core fusion system.

[0216] A computer-readable storage medium according to an embodiment of the present invention can execute a card-type quick application deployment method based on a multi-core fusion system provided by an embodiment of the method of the present invention, can execute any combination of implementation steps of the method embodiment, and has the corresponding functions and beneficial effects of the method.

[0217] The embodiment of the present invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the device performs Figure 1 The card-type quick application deployment method based on a multi-core fusion system is shown.

[0218] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the above-mentioned boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0219] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the above-mentioned functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the routine skills of an engineer. Therefore, a person skilled in the art can implement the present invention set forth in the claims using ordinary skills without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0220] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the above methods of each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0221] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0222] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable media on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0223] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0224] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0225] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0226] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the above embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A card-type quick application deployment method based on a multi-core fusion system, characterized in that: include: Decompose the quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies; Configuring the adaptive interface of each of the card modules and determining the constraint value of the adaptive interface; determining the compatibility between the kernel and each of the card modules according to kernel status information, performance requirement coefficients of each of the card modules, and the dependency between the kernel and each of the card modules; Determining a minimum deployment status value according to the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information, and kernel deployment environment complexity dimension information; Deploy and combine each of the card modules and the core according to the minimum deployment status value; The correlation degree is determined according to the multi-dimensional context data information, and the operation state of the card module is managed according to the correlation degree.

2. A card-type quick application deployment method based on a multi-core fusion system according to claim 1, characterized in that: The constraint value of the adaptive interface is calculated using a first mathematical formula, where the first mathematical formula is: Among them, I constraint is the constraint value, n is the number of functional items associated with the interface, α i is the importance weight coefficient of the i-th function item, F i input is the input data volume of the i-th function item, F i output is the output data volume of the i-th function item, γ i is the environmental adaptation coefficient, is the time dynamic adjustment coefficient, δ i is the network quality impact coefficient, χ i is the interface stability coefficient, λ new is the functional item interaction entropy coefficient, μ dynamix is the interface dynamic load adjustment coefficient, ν con is the context-aware interface adaptation coefficient, the constraint value I constraint ≤preset interface constraint threshold θ.

3. The card-type quick application deployment method based on a multi-core fusion system according to claim 2, characterized in that: The environmental adaptation coefficient γ is calculated by the second mathematical formula i , the second mathematical formula is: Among them, h is the number of hardware parameter dimensions, λ ij is the sensitivity coefficient of the i-th function item to the j-th hardware parameter, Hj is the actual value of the j-th hardware parameter, μ i is the threshold, κ ε is the hardware parameter interaction coefficient, ξ har is the dynamic interaction coefficient of hardware parameters.

4. The card-type quick application deployment method based on a multi-core fusion system according to claim 1, characterized in that: The kernel status information includes a compatibility coefficient, energy efficiency, security risk coefficient, kernel scalability coefficient, and kernel impact coefficient. The compatibility between the kernel and each of the card modules is calculated using a third mathematical formula. The third mathematical formula is: Among them, k ij is the degree of adaptation, w1 is the first weight coefficient, w2 is the second weight coefficient, w3 is the third weight coefficient, and w1+w2+w3=1, P i com is the compatibility coefficient of the i-th kernel, T j per is the performance requirement coefficient of the jth card module, E i ene is the energy efficiency of the i-th core, D ij dep The dependency of the i-th core and the j-th card module, is the safety risk coefficient, τ ij is the core scalability coefficient, and NH is the core impact coefficient.

5. A card-type quick application deployment method based on a multi-core fusion system according to claim 4, characterized in that: The kernel influence coefficient NH is calculated by a fourth mathematical formula, which is: NH=η new ×T synergy Among them, η new is the kernel resource dynamic allocation coefficient, T synergy is the inter-core synergy coefficient.

6. A card-type quick application deployment method based on a multi-core fusion system according to claim 4, characterized in that: The compatibility coefficient P is calculated by the fifth mathematical formula i com , the fifth mathematical formula is: Among them, N i prol is the number of protocols supported by the i-th kernel, N max-prol N is the maximum number of protocols supported by all kernels. i for N is the number of data formats supported by the i-th kernel. max-for S is the maximum number of data formats supported by all kernels. i cob is the synergy coefficient between the i-th core and other cores, η i is the kernel update frequency influence coefficient, σ i is the core ecological activity coefficient, ρ e-g It is the core ecological growth coefficient.

7. The card-type quick application deployment method based on a multi-core fusion system according to claim 1, characterized in that: The minimum deployment status value is calculated by a sixth mathematical formula, which is: Among them, O opt is the minimum deployment status value, C i dep is the deployment cost of the i-th kernel, q is the number of system resource limitation dimensions, ξ sj is the influence weight of the sth system resource limitation dimension on the jth card module, L sj is the limit value of the jth card module in the sth system resource limit dimension, r is the number of system service quality indicator dimensions, w tj is the importance weight of the t-th system service quality indicator dimension to the j-th card module, M tj The target value of the jth card module in the tth system service quality indicator dimension, v is the number of kernel deployment environment complexity dimensions, u uj is the influence weight of the u-th environmental complexity dimension on the j-th card module, ν uj is the adaptation value of the j-th card module in the u-th environmental complexity dimension.

8. The card-type quick application deployment method based on a multi-core fusion system according to claim 1, characterized in that: The multi-dimensional context data information includes weights, current values, historical values, and average values ​​corresponding to several data dimensions. The correlation degree is calculated using a seventh mathematical formula, which is: Among them, R con is the correlation degree σ is the correlation degree adjustment coefficient, ρ k is the weight of the kth data dimension, D k cur is the current value of the kth data dimension, D k his is the historical value of the kth data dimension, D k ave is the average value of the kth data dimension, τk is the data fluctuation impact index, ν k is the data credibility coefficient, w k1 is the data correlation enhancement coefficient, φ new is the context data burstiness coefficient, γ uni is the context-specific coefficient, δ ene is the emergency context response coefficient, π log-ter is the long-term trend context adaptation coefficient.

9. A card-type quick application deployment system based on a multi-core fusion system, characterized in that: include: A quick app decomposition module is used to decompose a quick app into several functionally independent card modules, determine the dependencies between the card modules, and determine the deployment order of the card modules based on the dependencies; An adaptive interface configuration module, configured to configure the adaptive interface of each of the card modules and determine the constraint value of the adaptive interface; a compatibility determination module, configured to determine the compatibility between the kernel and each of the card modules based on kernel status information, a performance requirement coefficient of each of the card modules, and a dependency between the kernel and each of the card modules; A minimum deployment status value module is used to determine a minimum deployment status value according to the adaptability, kernel deployment cost, system resource limitation dimension information, system service quality indicator dimension information and kernel deployment environment complexity dimension information; A deployment combination module, configured to deploy and combine each of the card modules and the core according to the minimum deployment status value; The correlation determination module is used to determine the correlation according to the multi-dimensional context data information, and manage the operation status of the card module according to the correlation.

10. A device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements a card-type quick application deployment method based on a multi-core fusion system according to any one of claims 1 to 8.

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