Method and system for multitasking processing of intelligent terminal

Abstract

The application provides a multi-task switching processing method and system of a smart terminal. The method comprises the following steps: acquiring application running data and system memory data of the smart terminal, classifying the applications of the smart terminal to obtain an application state management table; performing resource pre-allocation on the application state management table based on the available amount of system resources of the smart terminal and a user operation event to obtain resource scheduling information; identifying a task switching parameter of the user operation event, and combining the resource scheduling information to construct a state switching instruction for a target application; and performing task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction to obtain task switching information. The application can identify a task switching parameter of a user operation event, and combine resource scheduling information to construct a state switching instruction for a target application.

Description

Technical Field

[0001] This application relates to the technical field of smart terminals, and in particular to a multi-task switching processing method and system for smart terminals. Background Technology

[0002] Smart terminal operating systems typically employ application scheduling mechanisms based on process priority and recent usage records. However, these mechanisms still present numerous challenges in complex multitasking scenarios. Existing methods often rely on static resource allocation strategies or simple recent usage record management, failing to fully consider the dynamic coordination between the actual running state of applications, user operation intentions, and real-time system resource status. This leads to increased switching latency and frequent application restarts when resources are limited, severely impacting user experience and system energy efficiency. Summary of the Invention

[0003] To overcome the problems existing in related technologies, this application provides a multi-task switching processing method and system for smart terminals, which can construct state switching instructions for target applications by recognizing task switching parameters of user operation events and combining them with resource scheduling information.

[0004] This application also provides a multi-task switching processing method for a smart terminal, characterized in that it includes:

[0005] Acquire application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain an application status management table;

[0006] Based on the system resource availability and user operation events of the smart terminal, the application status management table is pre-allocated to obtain resource scheduling information.

[0007] The task switching parameters of the user operation event are identified, and a state switching instruction is constructed for the target application in combination with the resource scheduling information.

[0008] Based on the state switching instruction, the target application of the smart terminal is rendered and the process is scheduled to obtain task switching information.

[0009] Preferably, the step of acquiring application running data and system memory data of the smart terminal, classifying the launched applications of the smart terminal, and obtaining an application status management table includes:

[0010] The application's runtime data is analyzed to determine its activity level and resource usage, thus obtaining an activity status identifier.

[0011] Based on the system memory data and the activity status identifier, the smart terminal is identified to identify running background applications, resulting in frozen background applications, active background applications, and dormant applications.

[0012] The state parameters of the frozen background application, the active background application, and the dormant application are matched respectively to obtain a set of state parameters.

[0013] Based on the set of status parameters, the process status of the background frozen application, the background active application, and the dormant application is marked to obtain the application status management table.

[0014] Preferably, the step of identifying running background applications on the smart terminal based on the system memory data and the activity status identifier to obtain frozen background applications, active background applications, and dormant applications includes:

[0015] The system memory data is subjected to memory state detection to obtain a memory state vector and process liveness identifier;

[0016] Based on the activity status identifier and the process survival identifier, the background applications of the smart terminal are filtered to obtain the dormant application and the candidate background application set.

[0017] Based on the memory state vector, the resource consumption clustering of the candidate background application set is performed to obtain a first subset of consuming applications and a second subset of consuming applications.

[0018] The first subset of consuming applications and the second subset of consuming applications are sorted by freezing priority according to a preset freezing threshold to obtain the background frozen applications and the background active applications.

[0019] Preferably, the step of pre-allocating resources to the application status management table based on the system resource availability and user operation events of the smart terminal to obtain resource scheduling information includes:

[0020] The application status management table is prioritized according to the user operation events to obtain the application scheduling queue.

[0021] Based on the available system resources, the application scheduling queue is pre-allocated resources to obtain a pre-allocated resource table;

[0022] The system memory data is dynamically partitioned based on the pre-allocated resource table to obtain memory allocation information;

[0023] By combining the memory allocation information with the application scheduling queue, resource scheduling information is obtained.

[0024] Preferably, the step of dynamically partitioning the system memory data according to the pre-allocated resource table to obtain memory allocation information includes:

[0025] The system memory data is partitioned based on the resource requirements of each application in the pre-allocated resource table to obtain memory partition parameters;

[0026] Based on the pre-allocated resource table, the memory partition parameters are used to divide the memory boundaries, and first memory partition information is generated.

[0027] The first memory partition information is optimized for adjacent partition overlap to generate the second memory partition information;

[0028] The system memory data is allocated based on the second memory partition information to obtain the memory allocation information.

[0029] Preferably, the step of identifying the task switching parameters of the user operation event and constructing a state switching instruction for the target application in conjunction with the resource scheduling information includes:

[0030] The user operation events are analyzed to identify the operation intent and obtain application switching parameters.

[0031] The application switching parameters are matched with the application status management table to obtain a list of applications to be switched.

[0032] Based on the resource scheduling information, the list of applications to be switched is subjected to resource verification and priority sorting to obtain a ready application queue.

[0033] Based on the ready application queue, the system memory data, and the application switching parameters, the target application is reconstructed for switching context and instructions are constructed to obtain the state switching instruction.

[0034] Preferably, the step of performing task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction to obtain task switching information includes:

[0035] According to the state switching instruction, the target application is loaded with rendering context and graphics resources to obtain graphics buffer data;

[0036] Based on the graphics buffer data, interface layers are synthesized to obtain synthesized frame data;

[0037] Display control parameters are obtained by controlling the display output of the target application using the synthesized frame data.

[0038] By combining the display control parameters and the state switching instructions, the process state of the target application is transitioned to obtain the task switching information.

[0039] Preferably, the step of controlling the display output of the target application through the synthesized frame data to obtain display control parameters includes:

[0040] Based on the synthesized frame data, the target application is synchronized to obtain synchronization signal parameters.

[0041] Based on the synchronization signal parameters, the output frequency is adapted and the color conversion is corrected for the synthesized frame data to obtain the color frame rate parameters.

[0042] The target application is subjected to image transmission and resource scheduling using the color frame rate parameters to obtain image scheduling parameters.

[0043] The synchronization signal parameters and the image scheduling parameters are integrated to generate the display control parameters.

[0044] This application also provides a multi-task switching processing apparatus for a smart terminal, applied to the multi-task switching processing method for a smart terminal described in any one of the above claims, comprising:

[0045] The identification module is used to acquire application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain an application status management table.

[0046] The parsing module is used to pre-allocate resources in the application status management table based on the system resource availability and user operation events of the smart terminal, and obtain resource scheduling information.

[0047] The processing module is used to identify the task switching parameters of the user operation event and construct a state switching instruction for the target application in combination with the resource scheduling information;

[0048] A construction module is used to perform task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction, and obtain task switching information.

[0049] The technical solution provided in this application may include the following beneficial effects:

[0050] This application categorizes launched applications by acquiring application runtime data and system memory data, establishing an application status management table. This enables fine-grained differentiation and management of different application runtime states, providing an accurate basis for subsequent resource scheduling. Based on system resource availability and user operation events, the application status management table performs resource pre-allocation, generating resource scheduling information to ensure that the required resources are ready when switching applications, effectively reducing latency and stuttering during the switching process. By identifying task switching parameters from user operation events and combining them with resource scheduling information, state switching instructions are constructed for the target application, achieving intelligent switching decisions based on real-time system status and user intent, improving the accuracy of operation response. Based on the state switching instructions, task rendering and process scheduling are performed on the target application, completing graphical interface rendering and process state transitions, ensuring the smoothness of the switching process and system stability, and improving multi-tasking efficiency and user experience.

[0051] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0052] The above and other objects, features and advantages of this application will become more apparent from the more detailed description of exemplary embodiments thereof in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments thereof.

[0053] Figure 1 This is a flowchart illustrating a multitasking switching method for a smart terminal according to an embodiment of this application;

[0054] Figure 2 This is a structural diagram of a multitasking switching processing system for a smart terminal, as shown in an embodiment of this application. Detailed Implementation

[0055] Preferred embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0056] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0057] It should be understood that although the terms "first," "second," "third," etc., may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0058] Reference Figure 1 As shown, this application provides a multi-task switching processing method for a smart terminal, characterized by comprising:

[0059] Step S10: Obtain application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain the application status management table;

[0060] Step S20: Based on the system resource availability of the smart terminal and user operation events, pre-allocate resources in the application status management table to obtain resource scheduling information;

[0061] Step S30: Identify the task switching parameters of user operation events, and construct state switching instructions for the target application in combination with resource scheduling information;

[0062] Step S40: Perform task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction to obtain task switching information.

[0063] Based on the steps described above, the detailed process is as follows:

[0064] Step S10:

[0065] Application runtime data includes CPU utilization, network connection status, service activity, and interface visibility parameters for each application process; system memory data covers total memory, available memory, cache usage, and memory resident size for each process.

[0066] Data acquisition is achieved through the system kernel interface and runtime environment monitoring module, forming a raw data set. During the data processing phase, activity metrics analysis is performed on the application runtime data, generating activity status identifiers by calculating process activity frequency and resource consumption patterns; simultaneously, application behavior patterns are identified by combining memory pressure indicators from system memory data.

[0067] Based on activity status indicators and memory usage characteristics, background applications are divided into three status categories: frozen background applications refer to processes that are inactive and can be in deep hibernation; active background applications refer to processes that are invisible but still perform background tasks or maintain network connectivity; and hibernating applications refer to processes that are completely inactive and whose resources can be reclaimed by the system.

[0068] The generated application status management table uses a structured data format to record the application package name, process ID, category label, and corresponding resource policy index. This table serves as the core metadata for multi-task management, providing a basis for subsequent resource allocation decisions.

[0069] Step S20:

[0070] System resource availability is obtained by monitoring multiple metrics such as available CPU core computing power, current free memory capacity, GPU rendering bandwidth, and network throughput, forming a system resource snapshot. User operation events include touch gestures, application switching triggers, and foreground application interactions, which are captured and parsed through the event bus into standardized descriptions containing event type, target application, and operation timestamp.

[0071] The resource pre-allocation engine first determines the basic resource quota based on the resource policy index of various applications in the application status management table; then, it elastically adjusts the quota by integrating the available system resources: allocating maintenance resource bandwidth to active background applications, setting resource reclamation thresholds for dormant applications, and adopting an on-demand allocation strategy for frozen background applications. User operation events participate in dynamic priority calculation, high-frequency interactive applications receive resource reservation weights, and sudden operations trigger the resource reallocation mechanism.

[0072] The generated resource scheduling information includes a memory block mapping table, CPU time slice allocation sequence, I / O bandwidth quota, and network connection priority matrix. This information is transmitted to the system scheduler in binary instruction set format to provide resource guarantees for application state switching.

[0073] Step S30:

[0074] User interaction events are captured by the input subsystem, including raw data such as touch coordinate trajectories, gesture types, and operation time sequences. The event parsing module performs feature extraction and pattern recognition on the raw data, parsing out the target application identifier, switching direction (forward / backward), and switching urgency parameters. The task switching parameters are matched and verified against resource scheduling information. By querying the pre-allocated resource mapping table in the resource scheduling information, the memory reservation status, CPU time slice availability, and graphics rendering resource readiness of the target application are confirmed.

[0075] The state switching instructions are constructed using a layered strategy: the base layer contains process activation flags and priority setting parameters, the middle layer defines the rendering priority and context loading order for user interface restoration, and the top layer integrates resource binding instructions and exception handling strategies.

[0076] A conflict detection mechanism is executed during instruction generation. When resource conflicts or state inconsistencies are detected, a resource reallocation negotiation process is triggered to ensure the executability of the instructions. The final generated state switching instruction adopts a structured message format, which includes the target application process identifier, resource access token, rendering context descriptor, and expected completion timestamp. This instruction serves as the unified execution basis for the system scheduler and rendering engine.

[0077] Step S40:

[0078] After receiving the state switching instruction, the process scheduler adjusts the scheduling strategy of the target application according to the process priority parameter in the instruction, and performs process context restoration operations, including register state reconstruction, memory mapping table update, and thread priority promotion. Simultaneously, the rendering engine loads the target application's interface layer data according to the rendering context descriptor in the instruction, performs texture preprocessing, geometric transformation, and composition tree construction operations, and generates framebuffer data.

[0079] Based on the resource access token in the command, the resource manager binds the pre-allocated CPU cores, memory blocks, and GPU computing units to the target application process. During execution, real-time performance monitoring is implemented, collecting metrics such as frame rendering latency, process activation time, and resource utilization.

[0080] The generation of task switching information consists of two parts: basic status data records the switching completion time, resource binding status, and rendering output quality; performance metric data includes scheduling latency percentage, frame drop rate, and memory swap count. The final output task switching information adopts a timestamped binary log format, which serves as both proof of the switching operation's completion and provides basic data for performance analysis and system optimization.

[0081] This application provides a multi-task switching method for smart terminals. By acquiring application runtime data and system memory data, it categorizes launched applications and establishes an application status management table. This enables fine-grained differentiation and management of different application runtime states, providing accurate data for subsequent resource scheduling. Based on system resource availability and user operation events, the application status management table is pre-allocated to generate resource scheduling information, ensuring that the required resources are ready during application switching and effectively reducing latency and stuttering. By identifying task switching parameters from user operation events and combining them with resource scheduling information, a state switching command is constructed for the target application. This enables intelligent switching decisions based on real-time system status and user intent, improving the accuracy of operation response. Based on the state switching command, the target application is rendered and its processes are scheduled, completing graphical interface rendering and process state transitions. This ensures smooth switching and system stability, improving multi-task processing efficiency and user experience.

[0082] In one embodiment, application runtime data and system memory data of the smart terminal are obtained, and the launched applications on the smart terminal are categorized to obtain an application status management table, including:

[0083] The monitoring module collects runtime characteristic data of the application process, including but not limited to CPU time slice utilization, number of active threads, service binding status, duration of user interface visibility, and network connection activity. An adaptive strategy is used for data collection frequency, increasing the sampling rate during high-frequency interaction phases and decreasing the sampling frequency during background silent phases to reduce system overhead.

[0084] The application activity calculation combines a comprehensive time decay factor and event trigger weights, giving higher weight to recent interaction events and reducing the impact factor of historical behavior data using an exponential decay model. A multi-dimensional quantitative system is established for resource occupancy level assessment, assigning different weight coefficients to memory residency size, I / O operation frequency, and sensor call frequency, generating a resource occupancy score through linear combination.

[0085] The activity status identifier output adopts a three-state classification mechanism: applications with high activity and high resource consumption are marked as foreground active state, applications with low activity and high resource consumption are marked as potential background active state, and applications with low activity and low resource consumption are marked as dormant candidate state.

[0086] The identifier generation process incorporates a mechanism to avoid frequent state transitions; a state update is only triggered when the difference between the current state and the calculated state exceeds a preset threshold. The final output activity state identifier includes the application package name, process ID, activity score, resource level label, and state confidence index, providing a basis for subsequent classification.

[0087] Perform a dual analysis of memory stress status assessment and application behavior pattern matching. System memory data parsing includes indicators such as total memory, available memory ratio, cache usage distribution, and swap space utilization. The current system memory load level is calculated using a memory stress model.

[0088] Activity status identifiers provide application-level behavioral feature inputs, which work together in the classification decision engine. The classification process employs a multi-level filtering mechanism: primary filtering excludes foreground applications and completely dormant applications based on activity status identifiers; secondary filtering combines memory pressure levels and application resource usage patterns to classify low-activity but high-memory-usage applications as background active applications, and low-activity and low-memory-usage applications as dormant application candidates; the final filtering stage introduces time-series behavior verification, continuously monitoring the network access behavior, sensor call records, and background service activities of dormant application candidates, ensuring that only applications with consistently no active events are officially marked as dormant applications.

[0089] The identification of background frozen applications employs an active detection mechanism, sending heartbeat detection packets to application processes that have been unresponsive for extended periods and have low resource consumption. Unresponsive processes are then added to the freeze queue. The classification results are output as three independent application lists, each containing an application identifier, a classification timestamp, and an associated resource policy index.

[0090] Establish a mapping relationship between application categories and resource management strategies, and achieve fine-grained control through a parameter configuration engine. For background frozen applications, the matched parameters include memory compression threshold, process priority decay coefficient, wake-up heartbeat detection interval, and maximum freeze duration. The parameter matching process refers to the historical behavior patterns of applications; the freeze depth is appropriately relaxed for frequently woken applications, while basic network access permissions are retained for critical background service applications.

[0091] The parameter configuration for background applications focuses on resource quota management, including CPU time slice allocation weight, memory cache retention size, I / O operation frequency limit, and network bandwidth allocation ratio. Parameter generation employs a dynamic adjustment mechanism, elastically scaling based on system load and real-time application needs. Resource quotas are automatically reduced under high load and appropriately increased under low load. Parameter matching for hibernation applications focuses on resource reclamation strategies, setting memory reclamation priority, process termination timeout threshold, state preservation integrity level, and wake-up / reconstruction latency tolerance.

[0092] The parameter matching process incorporates cross-application correlation analysis. Application groups with dependencies use collaborative parameter configuration to ensure consistency in application state transitions within the group. All parameters undergo compliance verification to avoid parameter conflicts or excessive resource allocation. The final output set of state parameters is stored using a key-value pair data structure, with each application corresponding to a set of parameter identifiers and their values. A dependency graph is established between parameters for query verification.

[0093] The parameter configuration is translated into system-executable control instructions through a kernel-level status marking mechanism. Process status marking employs a layered implementation strategy: the first layer sets process scheduling priority and CPU affinity; the second layer adjusts memory management policies, including page compaction flags and swapping priority; and the third layer configures I / O scheduling queue priorities and network access permissions.

[0094] Real-time conflict detection is performed during the marking process. When resource allocation conflicts or policy contradictions are detected, a parameter recalculation process is triggered to regenerate the coordination parameters. The application state management table is constructed using a multi-index structure, establishing a mapping relationship between the application identifier as the primary key and the process descriptor, resource policy identifier, and state parameters. Table fields include basic application attributes (package name, process ID), state category labels, parameter set pointers, last state update timestamps, and state transition history. A consistency protection mechanism is implemented during the maintenance of the management table, ensuring data integrity under multi-threaded access through atomic operations, while version control is established to support state rollback functionality.

[0095] The generated application status management table serves as the core basis for system resource scheduling, providing a complete status description and policy support for subsequent resource allocation and task switching. The table exists in shared memory, allowing multiple subsystems, including the system scheduler, memory manager, and network stack, to work together.

[0096] This embodiment achieves refined control over background applications through the construction of an application status management table and an application classification mechanism, effectively improving system resource utilization. Dynamic parameter matching and process status marking ensure that various applications receive matching resource strategies, avoiding resource conflicts and uneven allocation. A layered state recovery and resource scheduling scheme significantly reduces task switching latency and improves system response speed. Multi-dimensional data collaborative analysis and real-time monitoring mechanisms guarantee the accuracy of state judgment, enhancing the stability and energy efficiency of system operation in multi-tasking environments.

[0097] In one embodiment, the running background applications on the smart terminal are identified based on system memory data and activity status identifiers to obtain frozen background applications, active background applications, and dormant applications, including:

[0098] The memory management module collects real-time operational metrics of the memory subsystem, including physical memory page allocation status, cache hit rate, swap partition activity, and the trend of process working set size changes. Memory status monitoring employs a multi-dimensional sampling strategy, acquiring memory stress indicators, fragmentation levels, and allocation latency data at fixed time intervals to form a raw monitoring dataset.

[0099] During the data processing phase, the sampled data is normalized, converting indicators of different dimensions into standard units of measurement. A multi-dimensional vector representing the overall memory status is then generated through weighted aggregation. This vector contains structured data such as memory availability scores, allocation efficiency coefficients, and stress warning levels. Process liveness is determined based on process control block status information and scheduler activity records, combined with inter-process communication status and signal response latency.

[0100] The survival flag generation process incorporates a heartbeat detection mechanism, sending probe signals to suspected zombie processes and correcting the flag status based on response timeouts. The final output memory state vector is stored in floating-point array format, while the process survival flag records the survival status of each process in bitmap form. Both together constitute the basic input for subsequent classification decisions.

[0101] The initial application classification is performed, and filtering is carried out based on the status identifiers generated in the previous stage. The filtering process establishes a dual filtering mechanism: the primary filtering layer excludes terminated or invalid process instances based on the process liveness identifier, ensuring that all subsequent operation objects are active process entities; the secondary filtering layer uses the activity status identifier as the classification basis, removing processes identified as being in the foreground active state from the set to be classified, and retaining the set of processes with background running characteristics.

[0102] The identification of dormant applications employs strict criteria. Processes that simultaneously meet the criteria of low activity score, lack user interface components, and have no background service binding are marked as dormant candidates. The generation of the candidate background application set includes a resource consumption verification step, which checks the memory resident size and CPU usage history of processes that pass the initial screening, excluding process instances with negligible resource consumption.

[0103] The screening process employs dynamic threshold adjustment, automatically adjusting classification sensitivity based on the overall system load, and using stricter screening criteria under high memory pressure. The final output uses a split-set storage structure, with the dormant application set and the candidate background application set respectively recording the application identifier, classification timestamp, and screening criterion code.

[0104] A multi-dimensional threshold grouping mechanism is employed to differentiate resource usage patterns among candidate applications. Feature extraction analyzes key indicators such as memory working set size, cache usage ratio, memory access frequency, and page fault rate from the memory state vector for each application. The grouping operation categorizes applications into high-resource-consuming and low-resource-consuming groups based on preset resource consumption threshold boundaries.

[0105] High-resource-consumption groups correspond to application instances with large memory residency, high caching requirements, and frequent access; low-resource-consumption groups correspond to application instances with small memory usage and low access frequency. An outlier exclusion mechanism is implemented during the grouping process, and application instances whose metrics fluctuate beyond a reasonable range are handled separately to ensure the stability of the grouping results. Both final output application subsets include a list of application identifiers, a description of resource consumption characteristics, and grouping parameters, providing a basis for subsequent priority ranking.

[0106] Freezing policies are implemented based on resource consumption characteristics, with freezing thresholds dynamically adjusted according to available system memory capacity and current load. Priority ranking uses a multi-factor weighted calculation method, with parameters including application memory consumption, recent usage timestamp, user interaction frequency, and system policy requirements. High-resource-consuming applications in the first subset are prioritized for freezing, but applications performing critical tasks are exempt from freezing. Low-resource-consuming applications in the second subset are further differentiated based on their recent activity; applications with recent interaction records are marked as background active applications, while applications with no long-term activity are included in the freezing scope.

[0107] The sorting process incorporates a system stability guarantee mechanism, freezing applications essential for maintaining system functionality to ensure core functions remain unaffected. The final generated list of frozen background applications is sorted in descending order of freezing urgency, while the list of active background applications is sorted by resource requirement priority. Both lists record the application identifier, freezing priority weight, and resource configuration parameters.

[0108] This embodiment achieves accurate identification and classification of background applications through collaborative analysis of memory state vectors and process liveness indicators, effectively improving the precision of system resource management. A resource consumption feature grouping mechanism is employed to distinguish between high- and low-consumption application groups based on actual memory usage patterns, providing a reliable basis for differentiated resource strategies. Dynamically adjusted freeze thresholds and multi-factor priority ranking ensure that high-resource-consuming applications are frozen in a timely manner, while guaranteeing the continuous operation of critical background tasks. This application classification method based on real-time memory status significantly reduces unnecessary memory usage, increases the proportion of available system memory, and enhances overall response performance and energy efficiency in multi-tasking environments.

[0109] In one embodiment, resource pre-allocation is performed on the application state management table based on the system resource availability of the smart terminal and user operation events to obtain resource scheduling information, including:

[0110] The event parsing module captures raw event data generated by user interactions, including touch coordinate trajectories, gesture types, operation time sequences, and target application identifiers. Event data processing employs a multi-level filtering mechanism to remove invalid operation events and noisy data, retaining valid events with clear interaction intentions.

[0111] The priority calculation engine generates application priority scores by combining event type weights, operation frequency coefficients, and time decay factors. Applications with high-frequency interactions receive higher priorities, while applications that have not been used for a long time automatically have their priorities decayed. The sorting algorithm employs a stable sorting strategy, maintaining the original relationships between applications while ensuring the priority order.

[0112] The application scheduling queue is generated using a doubly linked list data structure. High-priority application instances are stored at the head of the queue, and low-priority applications are stored at the tail. Each queue node records the application identifier, priority score, queue position index, and timestamp information, supporting fast insertion and deletion operations. A dynamic adjustment mechanism is implemented during queue maintenance. When a new user operation event is added or the application state changes, the priority is recalculated and the queue order is adjusted to ensure that the queue reflects current user operation preferences in real time.

[0113] The system performs matching calculations between resource demands and system supply. The system resource availability monitoring module collects resource indicators in real time, such as CPU core idle computing power, available memory capacity, GPU rendering bandwidth, and network connection count. The resource allocation strategy implements differentiated allocation based on the priority order in the application scheduling queue. High-priority applications receive sufficient resource reservations, while low-priority applications are allocated basic guaranteed resources.

[0114] The pre-allocation calculation employs a progressive allocation algorithm. In the initial stage, a basic resource guarantee is allocated to each application. Subsequently, the resource quota is gradually increased according to priority until the application's needs are met or the system's resource limit is reached. The resource conflict resolution mechanism detects and handles resource contention during the allocation process. When multiple applications compete for the same resource, the higher-priority application receives priority allocation.

[0115] The pre-allocated resource table uses a matrix storage structure, with rows corresponding to application identifiers and columns recording the allocation amounts of various resources, including memory block address ranges, CPU time slice ratios, GPU computing unit identifiers, and network bandwidth values. After the table is generated, an integrity check is performed to ensure that resource allocation does not overflow and meets minimum resource guarantee requirements.

[0116] The memory management unit performs physical memory reorganization and allocation, and the pre-allocated resource table provides memory requirement specifications and priority indicators for each application. Dynamic partitioning operations use address space layout randomization technology combined with fixed-size memory pool management to divide memory blocks of different sizes according to the memory requirements of applications. High-priority applications are allocated contiguous physical memory blocks to reduce address translation overhead, while low-priority applications are allocated using a discrete memory page combination method.

[0117] During partitioning, memory defragmentation is implemented, reducing external fragmentation and improving memory utilization through page migration and merging operations. The generated memory allocation information includes detailed mapping records; the starting address, size, access permissions, and caching strategy of each application's corresponding memory block are precisely recorded. Allocation results are verified through a security check mechanism to ensure no address conflicts or permission out-of-bounds accesses, while maintaining memory access consistency. The final output memory allocation information is stored using a hierarchical index structure, supporting quick lookup of corresponding memory resource configurations by application identifier.

[0118] The system coordinates the allocation of computing and memory resources to achieve a complete mapping between applications and resources. The resource binding process employs atomic operations to ensure data consistency, associating process descriptors in the application scheduling queue with the address spaces in the memory allocation information. Binding strategies are differentiated based on application priority; high-priority applications receive exclusive resource binding guarantees, while low-priority applications utilize resource sharing and time-slicing multiplexing strategies.

[0119] The binding operation encompasses system-level operations such as CPU core affinity settings, memory mapping table updates, I / O channel allocation, and interrupt request configuration. Resource scheduling information is generated using a binary instruction encoding format, containing a complete set of resource descriptors, scheduling priority parameters, resource release callback pointers, and exception handling routine addresses. The final generated resource scheduling information is submitted to the kernel scheduler via a system call interface, serving as the authoritative basis for resource scheduling during task execution and ensuring accurate resource allocation and reliable reclamation during application state transitions.

[0120] This embodiment achieves intelligent generation of application scheduling queues through a user operation event-driven priority sorting mechanism, ensuring that resource allocation aligns with user operation intentions. A pre-allocation strategy aware of system resource availability is employed to match resource allocation with actual system load, improving resource utilization. Dynamic memory partitioning technology optimizes memory space layout, reducing memory fragmentation and improving access efficiency. A resource binding mechanism enables collaborative management of computing resources and memory space, ensuring a stable resource supply for high-priority applications. This approach significantly reduces resource allocation latency during task switching, improving system response speed and operational stability in multi-tasking environments.

[0121] In one embodiment, the system memory data is dynamically partitioned based on a pre-allocated resource table to obtain memory allocation information, including:

[0122] The memory management unit parses the memory requirements of each application recorded in the pre-allocated resource table, including memory block size requirements, access frequency patterns, and data alignment characteristics. Partition calculation employs an address space optimization algorithm, comprehensively considering physical memory layout characteristics and application access patterns to calculate the optimal memory block partitioning scheme. During the calculation process, the principle of maximizing space utilization is implemented, grouping and merging memory requirements of similar sizes to reduce memory fragmentation.

[0123] The memory partitioning parameters include the partition start address, partition size, partition type identifier, and access permission settings. After the parameters are generated, boundary alignment checks are performed to ensure that all partition addresses comply with the processor architecture's memory alignment requirements. The parameter set is stored in a tree structure, supporting fast lookup and dynamic adjustment.

[0124] The specific address space partitioning operation is performed. Based on the priority identifiers and memory requirement characteristics of each application in the pre-allocated resource table, the precise location of each partition in the physical address space is determined. Boundary partitioning adopts a priority-driven allocation strategy, allocating contiguous memory blocks to high-priority applications and using discrete address allocation for low-priority applications.

[0125] During the partitioning process, security isolation checks are implemented to ensure sufficient security isolation bands between different application partitions, preventing out-of-bounds memory access. The first memory partition information records the start address, end address, partition size, application identifier, and protection attributes of each partition. Information encoding uses a combination of bitmaps and descriptors, ensuring both storage efficiency and fast access. After information generation, overlap detection and conflict resolution are performed to ensure that the address ranges of each partition do not overlap.

[0126] The memory optimization engine performs fine-tuning of the partition layout, performing overlap detection and space reorganization on the address range data recorded in the first memory partition information. The optimization operation uses a sliding window algorithm to scan the boundaries of adjacent partitions, detecting partition pairs with overlapping addresses or excessively large gaps. For detected overlapping areas, partition boundaries are recalculated and address offsets are adjusted to ensure that each partition maintains a safe isolation distance.

[0127] The space reorganization process comprehensively considers partition access frequency and the principle of data locality, adjusting high-frequency access partitions to low-latency memory areas and merging low-frequency access partitions into large-capacity storage areas. During optimization, defragmentation is implemented, filling partition gaps by moving free memory blocks to improve memory space continuity and utilization. The second memory partition information, based on the first version, adds optimization identifiers, adjusted boundary coordinates, and defragmentation records, using incremental updates to maintain version association with the original data. Information encoding uses a differential storage format, recording only changed parameters to reduce storage overhead.

[0128] The actual allocation and mapping of physical memory is performed, and memory page allocation is executed based on the address range parameters in the second memory partition information. The allocation process employs a multi-level page table management mechanism, creating independent page table entries for each partition and setting corresponding access permissions and caching policies. The memory mapping operation includes establishing the virtual address to physical address translation mapping and configuring memory protection domains.

[0129] A real-time verification mechanism is implemented during the allocation process, using a hardware memory management unit to verify the correctness of address mappings and the compliance of permission settings. Memory allocation information records the actual physical address range, page table entry pointer, memory attribute identifier, and allocation timestamp for each partition. Information storage employs a hierarchical index structure to support fast lookups and dynamic adjustments. The final generated memory allocation information is submitted to the system kernel through the memory management interface, serving as the authoritative basis for subsequent memory access and management.

[0130] This embodiment achieves fine-grained memory space partitioning through memory partitioning driven by a pre-allocated resource table, significantly improving memory utilization and reducing external fragmentation. It employs a combination of memory boundary partitioning and overlap optimization to ensure conflict-free and secure isolation between application partitions, enhancing system stability. Adjacent partition optimization improves memory access locality and reduces address translation overhead. The dynamic memory allocation mechanism works in conjunction with the hardware memory management unit to ensure the accuracy of memory mapping and the security of access permissions.

[0131] This embodiment achieves fine-grained memory space partitioning through memory partitioning driven by a pre-allocated resource table, significantly improving memory utilization and reducing external fragmentation. It employs a combination of memory boundary partitioning and overlap optimization to ensure conflict-free and secure isolation between application partitions, enhancing system stability. Adjacent partition optimization improves memory access locality and reduces address translation overhead. The dynamic memory allocation mechanism works in conjunction with the hardware memory management unit to ensure the accuracy of memory mapping and the security of access permissions. This method effectively improves the efficiency and reliability of memory allocation in multi-tasking environments, providing stable memory resource guarantees for application switching.

[0132] In one embodiment, task switching parameters that identify user operation events are combined with resource scheduling information to construct state switching instructions for the target application, including:

[0133] The input event processing module captures raw interaction data, including touch coordinate sequences, gesture trajectory features, and operation time information. Event parsing employs a multi-layered analysis architecture: the bottom layer processes raw signal filtering and denoising; the middle layer performs gesture pattern classification and trajectory feature extraction; and the top layer combines application context for intent inference. The intent recognition engine integrates temporal behavior analysis and temporal feature matching to convert continuous operation event sequences into discrete intent identifiers.

[0134] The generation of application switching parameters includes the target application identifier, switching direction vector, operation urgency score, and expected completion time requirement. Parameter encoding uses a structured data format, and all fields are normalized to ensure data range consistency. A confidence verification mechanism is introduced into the recognition process; low-confidence recognition results trigger a secondary verification process, and historical behavior pattern matching improves recognition accuracy.

[0135] The system performs a mapping and matching between the target application identifier and known applications in the system. The application status management table provides status records and attribute descriptions for all applications. The matching operation adopts a two-way query mechanism. First, it performs an exact match in the management table based on the target application identifier in the application switching parameters to obtain the status attributes and resource policies of the corresponding application. Second, it performs associated application discovery based on the switching direction vector to identify potential application instances related to the current operation context.

[0136] The matching process implements state consistency verification to ensure that the target application is in a switchable state, excluding application instances that have been terminated or are in an abnormal state. The list of applications to be switched is generated using a priority sorting structure, with the primary target application at the top of the list and related applications arranged in descending order of relevance. List nodes record the application identifier, matching confidence score, state verification result, and association weight coefficient, supporting rapid access and decision-making in subsequent processing stages.

[0137] The resource verification module performs availability checks on application resource requirements, and the resource scheduling information provides the current system resource allocation status and details of reserved resources. Resource verification employs a multi-dimensional verification mechanism, checking the memory block reservation status, CPU time slice allocation, GPU rendering resource readiness, and network connection availability of the application to be switched.

[0138] During the verification process, conflict detection and resolution are implemented. Dynamic adjustments are made to address detected resource contention, ensuring critical applications receive necessary resources through resource reallocation or priority negotiation. Priority ranking is calculated using a multi-factor weighted calculation, taking into account the urgency of application switching parameters, resource verification results, and historical performance data, to generate the final priority sequence.

[0139] The ready application queue is stored using a doubly linked list structure. Each queue node contains an application identifier, resource verification result, priority score, and a summary of resource requirements. The highest priority application is stored at the head of the queue, ensuring that its resources are fully ready. After the queue is generated, a consistency audit is performed to ensure that resource allocation perfectly matches application requirements.

[0140] The entire application runtime environment is rebuilt. System memory data provides the current memory layout and status information, and the ready application queue identifies the target application and its resource bindings. The context reconstruction process includes process control block recovery, memory mapping table reconstruction, register state initialization, and runtime parameter configuration. An incremental recovery strategy is used to prioritize the recovery of critical context components.

[0141] The instruction building engine generates state transition instructions containing complete operation sequences. These instructions cover resource activation commands, state recovery parameters, exception handling procedures, and performance monitoring instructions. The instruction encoding adopts a layered structure: the base layer contains hardware operation instructions, the middle layer defines resource management parameters, and the higher layer integrates policy control flags. The building process implements multiple verifications to ensure the completeness and executability of the instruction sequence. The final generated state transition instructions are submitted to the kernel scheduler through the system interface, serving as the final execution basis for application state transitions.

[0142] This embodiment accurately parses user interaction behavior and generates complete application switching parameters through operation intent recognition and multi-level event analysis, providing reliable input for subsequent processing. Application matching and status verification are performed using an application status management table to ensure that all applications to be switched are in a normally switchable state. Resource availability verification and conflict resolution are performed based on resource scheduling information to ensure the stability of resource supply during application switching. Through context reconstruction and hierarchical instruction construction, state switching instructions containing complete operation sequences are generated to ensure accurate execution of application state switching.

[0143] In one embodiment, task rendering and process scheduling are performed on the target application of the smart terminal according to the state switching instruction to obtain task switching information, including:

[0144] The system analyzes the rendering parameters and resource mapping relationships contained in the state transition instructions for graphics rendering, extracting the UI element descriptors and graphics resource identifiers required by the target application. The rendering context restoration operation reconstructs the last known UI state of the target application, including the view hierarchy, control property configurations, and animation transition parameters.

[0145] The graphics resource loading module retrieves texture data, vector graphics resources, and font resources from the storage subsystem or network source based on resource identifiers, and performs decoding and preprocessing operations to generate directly renderable graphics objects. The generation of graphics buffer data adopts a double-buffering or multi-buffering mechanism, allocating contiguous memory space to store rendering output data. The buffer structure contains pixel data, depth information, and transparency channels.

[0146] After the data is generated, integrity checks and format conversions are performed to ensure that the buffer data meets the input requirements of the display hardware. The final output graphics buffer data includes a timestamp and version identifier for subsequent processing.

[0147] The system performs multi-layer compositing operations, collaboratively compositing the target application's graphics buffer data with system interface elements. The layer compositing engine employs a hardware-accelerated compositing pipeline, performing geometric transformations, color blending, and special effects processing based on layer priority, transparency settings, and transformation matrix parameters. The compositing process utilizes a dynamic optimization strategy, caching and reusing static layers, and employing incremental updates for dynamic layers to reduce computational overhead.

[0148] The generation of composite frame data includes complete frame pixel data, synchronization signal timing information, and display parameter configurations, with the data format conforming to the display controller's input specifications. After the compositing operation is complete, visual quality verification is performed, checking color consistency, edge smoothness, and inter-frame coherence to ensure the output frame data meets display quality standards. The final generated composite frame data includes a display timestamp and performance metadata, providing a ready-to-use frame data source for the display output stage.

[0149] The display management module receives composite frame data and parses the pixel information and synchronization timing parameters contained within it. Display output control works in conjunction with the display driver via a hardware abstraction layer interface to convert frame data into a signal format that conforms to the physical display device's requirements. Control parameter generation includes refresh rate settings, color space mapping, brightness adjustment coefficients, and power management strategies; these parameters are dynamically adjusted based on the characteristics of the displayed content and the device's status.

[0150] The output timing calibration mechanism ensures precise alignment of frame data with the vertical synchronization signal, preventing display tearing or jitter. The power optimization module intelligently adjusts backlight intensity and chip operating frequency based on the complexity of the frame content, reducing power consumption while maintaining display quality. The final output of display control parameters uses a device-independent standardized description format, containing all necessary configuration commands and status feedback information, providing complete control data for the display hardware.

[0151] The process performs a complete state transition for the application process, displays control parameters to provide final state confirmation via the interface, and includes all control information required for process scheduling in the state transition instructions. The process state transition operation first suspends the currently running process, saving its complete execution context, including register states, memory mapping information, and kernel object handles.

[0152] Based on the resource binding relationships in the state switching instructions, the priority of the target application process is elevated to the foreground running level, restoring its memory mapping table and hardware resource access permissions. The migration process employs atomic operations to ensure state consistency, guaranteeing a seamless transition back to the previous execution state after process recovery. The task switching information is generated and records complete migration process data, including process switching timestamps, resource reallocation results, displayed verification metrics, and exception handling records. The output task switching information uses a structured log format, serving both as proof of the completion of this switching operation and providing a data foundation for system performance analysis and optimization.

[0153] In one embodiment, display control parameters are obtained by controlling the display output of a target application using synthesized frame data, including:

[0154] The timing controller parses the frame timing information and vertical synchronization markers contained in the synthesized frame data to generate a timing control signal that matches the refresh cycle of the physical display device. The synchronization coordination operation employs a phase-locked mechanism to precisely align the application frame output timing with the display hardware refresh cycle, eliminating timing deviations during frame transmission.

[0155] The generation of synchronization signal parameters includes the horizontal synchronization pulse width, the vertical synchronization leading edge position, the blanking interval setting, and the pixel clock division coefficient. These parameters are dynamically adjusted according to the characteristics of the display device and the complexity of the frame content. The parameter calibration process implements jitter suppression and drift compensation to ensure the stability and accuracy of the output synchronization signal. The final generated synchronization signal parameters adopt a hardware-recognizable register configuration format.

[0156] The system performs dual adaptation of the display content in both the frequency and color domains, with synchronization signal parameters providing a reference timing framework. Output frequency adaptation performs frame rate conversion and motion compensation processing on the synthesized frame data based on the target display device's native refresh rate characteristics, ensuring smooth display of content at different frame rates.

[0157] The color conversion and correction operation, based on the display panel's color gamut characteristics and gamma response curve, converts the frame data color space to a device-dependent color space, performing color accuracy calibration and white balance adjustment. The generation of color frame rate parameters includes the final output frame rate value, color map index, gamma correction coefficients, and dynamic range compression parameters. These parameters undergo multiple verifications to ensure perfect matching with the display hardware characteristics. Parameter encoding uses a layered structure: the base layer contains the configuration values ​​required by the device driver, and the enhancement layer records color management metadata.

[0158] The physical transmission control of image data is performed by the display transmission engine, and the color frame rate parameter provides a benchmark for transmission timing and quality requirements. The image transmission operation adopts a pipelined architecture, dynamically adjusting the data transmission bandwidth and buffer size according to the output frame rate value in the color frame rate parameter to ensure that frame data is continuously and stably transmitted to the display controller.

[0159] The resource scheduling module allocates corresponding hardware resources based on the color map index and gamma correction coefficients, including color processing units, memory bandwidth quotas, and bus access priorities. An error detection and retransmission mechanism is implemented during transmission to correct bit errors or packet loss in real time. The generation of image scheduling parameters includes data transmission channel identifiers, memory bandwidth allocation values, bus occupancy time slices, and error correction strategies. These parameters are output in a control register configuration format after transmission quality verification.

[0160] The final synthesis of display control information is performed, with synchronization signal parameters providing the basis for timing control and image scheduling parameters providing the resource allocation scheme. Parameter integration employs a configuration fusion algorithm, which merges the bit fields and resolves conflicts between the register configuration values ​​of the two types of parameters to generate a unified hardware control instruction set.

[0161] The integration process implements parameter consistency verification to ensure that timing control and resource allocation strategies are compatible and free from conflicts or contradictions. The generation of display control parameters includes complete display hardware configuration instructions, status monitoring flags, and exception handling entry addresses. Parameter encoding uses a binary format that can be directly loaded by the device driver. The final generated display control parameters are submitted to the hardware layer through the display control interface, serving as the final execution basis for display output operations and ensuring precise coordination between image display and timing control.

[0162] This embodiment ensures precise matching between the synthesized frame data and the hardware refresh cycle through a display synchronization coordination mechanism, eliminating display tearing and improving visual smoothness. Output frequency adaptation and color conversion correction technologies are employed to fully match the image output characteristics with the physical characteristics of the display panel, guaranteeing color accuracy and consistent motion performance. Through the coordinated management of image transmission and resource scheduling, efficient transmission of display data and rational allocation of hardware resources are achieved. The integrated generation of display control parameters provides a complete hardware control scheme, ensuring unified coordination of timing control, color management, and resource scheduling during the display output process.

[0163] Reference Figure 2 As shown, this application also provides a multitasking switching processing apparatus for a smart terminal, which is applied to the multitasking switching processing method of a smart terminal according to any of the above-mentioned methods, including:

[0164] The identification module is used to obtain application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain an application status management table.

[0165] The parsing module is used to pre-allocate resources in the application status management table based on the system resource availability and user operation events of the smart terminal, and obtain resource scheduling information.

[0166] The processing module is used to identify task switching parameters of user operation events and, in conjunction with resource scheduling information, construct state switching instructions for the target application.

[0167] The construction module is used to perform task rendering and process scheduling for the target application of the smart terminal according to the state switching instruction, and obtain task switching information.

[0168] This application provides a multi-task switching processing system for smart terminals. By acquiring application runtime data and system memory data, it categorizes launched applications and establishes an application status management table. This enables fine-grained differentiation and management of different application runtime states, providing accurate data for subsequent resource scheduling. Based on system resource availability and user operation events, the system pre-allocates resources to the application status management table, generating resource scheduling information to ensure that the required resources are ready during application switching, effectively reducing latency and stuttering during the switching process. By identifying task switching parameters from user operation events and combining them with resource scheduling information, the system constructs state switching instructions for the target application, achieving intelligent switching decisions based on real-time system status and user intent, improving the accuracy of operation response. Based on the state switching instructions, the system performs task rendering and process scheduling for the target application, completing graphical interface rendering and process state transitions, ensuring smooth switching and system stability, and improving multi-task processing efficiency and user experience.

[0169] Regarding the system in the above embodiments, the specific manner in which each module performs its operations has been described in detail in the embodiments of the apparatus in the above embodiments, and the specific manner in which each module performs its operations has been described in detail in the embodiments of the method, and will not be elaborated further here.

[0170] The solution of this application has been described in detail above with reference to the accompanying drawings. In the above embodiments, the descriptions of each embodiment have different emphases; parts not described in detail in a certain embodiment can be referred to in the relevant descriptions of other embodiments. Those skilled in the art should also understand that the actions and modules involved in the specification are not necessarily essential to this application. Furthermore, it is understood that the steps in the method of this application embodiment can be adjusted, combined, and deleted according to actual needs, and the modules in the device of this application embodiment can be combined, divided, and deleted according to actual needs.

[0171] Furthermore, the method according to this application can also be implemented as a computer program or computer program product, which includes computer program code instructions for performing some or all of the steps in the method described above.

[0172] Alternatively, this application may be implemented as a non-transitory machine-readable storage medium (or computer-readable storage medium, or machine-readable storage medium) storing executable code (or computer program, or computer instruction code) thereon, which, when executed by a processor of an electronic device (or electronic device, server, etc.), causes the processor to perform part or all of the steps of the methods described above according to this application.

[0173] Those skilled in the art will also understand that the various exemplary logic blocks, modules, circuits, and algorithm steps described in connection with the present application can be implemented as electronic hardware, computer software, or a combination of both.

[0174] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems and methods according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0175] The various embodiments of this application have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A multi-task switching processing method for a smart terminal, characterized in that, include: Acquire application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain an application status management table; Based on the system resource availability and user operation events of the smart terminal, the application status management table is pre-allocated to obtain resource scheduling information. The task switching parameters of the user operation event are identified, and a state switching instruction is constructed for the target application in combination with the resource scheduling information. Based on the state switching instruction, the target application of the smart terminal is rendered and the process is scheduled to obtain task switching information. The resource pre-allocation based on the system resource availability and user operation events of the smart terminal to the application status management table yields resource scheduling information, including: The application status management table is prioritized according to the user operation events to obtain the application scheduling queue. Based on the available system resources, the application scheduling queue is pre-allocated resources to obtain a pre-allocated resource table; The system memory data is dynamically partitioned based on the pre-allocated resource table to obtain memory allocation information; By combining the memory allocation information with the application scheduling queue, resource scheduling information is obtained; The task switching parameters for identifying the user operation event, combined with the resource scheduling information, are used to construct a state switching instruction for the target application, including: The user operation events are analyzed to identify the operation intent and obtain application switching parameters. The application switching parameters are matched with the application status management table to obtain a list of applications to be switched. Based on the resource scheduling information, the list of applications to be switched is subjected to resource verification and priority sorting to obtain a ready application queue. Based on the ready application queue, the system memory data, and the application switching parameters, the target application is reconstructed for switching context and instructions are constructed to obtain the state switching instruction.

2. The multi-task switching processing method for a smart terminal according to claim 1, characterized in that, The process involves acquiring application runtime data and system memory data from the smart terminal, classifying the launched applications on the smart terminal, and obtaining an application status management table, including: The application's runtime data is analyzed to determine its activity level and resource usage, thus obtaining an activity status identifier. Based on the system memory data and the activity status identifier, the smart terminal is identified to identify running background applications, resulting in frozen background applications, active background applications, and dormant applications. The state parameters of the frozen background application, the active background application, and the dormant application are matched respectively to obtain a set of state parameters. Based on the set of status parameters, the process status of the background frozen application, the background active application, and the dormant application is marked to obtain the application status management table.

3. The multi-task switching processing method for a smart terminal according to claim 2, characterized in that, The step of identifying running background applications on the smart terminal based on the system memory data and the activity status identifier to obtain frozen background applications, active background applications, and dormant applications includes: The system memory data is subjected to memory state detection to obtain a memory state vector and process liveness identifier; Based on the activity status identifier and the process survival identifier, the background applications of the smart terminal are filtered to obtain the dormant application and the candidate background application set. Based on the memory state vector, the resource consumption clustering of the candidate background application set is performed to obtain a first subset of consuming applications and a second subset of consuming applications. The first subset of consuming applications and the second subset of consuming applications are sorted by freezing priority according to a preset freezing threshold to obtain the background frozen applications and the background active applications.

4. The multi-task switching processing method for a smart terminal according to claim 1, characterized in that, The step of dynamically partitioning the system memory data according to the pre-allocated resource table to obtain memory allocation information includes: The system memory data is partitioned based on the resource requirements of each application in the pre-allocated resource table to obtain memory partition parameters; Based on the pre-allocated resource table, the memory partition parameters are used to divide the memory boundaries, and first memory partition information is generated. The first memory partition information is optimized for adjacent partition overlap to generate the second memory partition information; The system memory data is allocated based on the second memory partition information to obtain the memory allocation information.

5. The multi-task switching processing method for a smart terminal according to claim 1, characterized in that, The step of performing task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction to obtain task switching information includes: According to the state switching instruction, the target application is loaded with rendering context and graphics resources to obtain graphics buffer data; Based on the graphics buffer data, interface layers are synthesized to obtain synthesized frame data; Display control parameters are obtained by controlling the display output of the target application using the synthesized frame data. By combining the display control parameters and the state switching instructions, the process state of the target application is transitioned to obtain the task switching information.

6. The multi-task switching processing method for a smart terminal according to claim 5, characterized in that, The step of controlling the display output of the target application using the synthesized frame data to obtain display control parameters includes: Based on the synthesized frame data, the target application is displayed and synchronized to obtain synchronization signal parameters; Based on the synchronization signal parameters, the output frequency is adapted and the color conversion is corrected for the synthesized frame data to obtain the color frame rate parameters. The target application is subjected to image transmission and resource scheduling using the color frame rate parameters to obtain image scheduling parameters. The synchronization signal parameters and the image scheduling parameters are integrated to generate the display control parameters.

7. A multi-task switching processing device for a smart terminal, characterized in that, The multitasking switching method applied to the smart terminal according to any one of claims 1-6 includes: The identification module is used to acquire application running data and system memory data of the smart terminal, classify the launched applications of the smart terminal, and obtain an application status management table. The parsing module is used to pre-allocate resources in the application status management table based on the system resource availability and user operation events of the smart terminal, and obtain resource scheduling information. The processing module is used to identify the task switching parameters of the user operation event and construct a state switching instruction for the target application in combination with the resource scheduling information; The construction module is used to perform task rendering and process scheduling on the target application of the smart terminal according to the state switching instruction, and obtain task switching information; The resource pre-allocation based on the system resource availability and user operation events of the smart terminal to the application status management table yields resource scheduling information, including: The application status management table is prioritized according to the user operation events to obtain the application scheduling queue. Based on the available system resources, the application scheduling queue is pre-allocated resources to obtain a pre-allocated resource table; The system memory data is dynamically partitioned based on the pre-allocated resource table to obtain memory allocation information; By combining the memory allocation information with the application scheduling queue, resource scheduling information is obtained; The task switching parameters for identifying the user operation event, combined with the resource scheduling information, are used to construct a state switching instruction for the target application, including: The user operation events are analyzed to identify the operation intent and obtain application switching parameters. The application switching parameters are matched with the application status management table to obtain a list of applications to be switched. Based on the resource scheduling information, the list of applications to be switched is subjected to resource verification and priority sorting to obtain a ready application queue. Based on the ready application queue, the system memory data, and the application switching parameters, the target application is reconstructed for switching context and instructions are constructed to obtain the state switching instruction.

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