Method, system and device for resource adaptation of an entertainment platform

Abstract

The application relates to the technical field of digital multimedia, and particularly discloses a resource adaptation method, system and device of an entertainment platform, wherein the method comprises the following steps: obtaining basic hardware parameter information and hardware bottom-layer physical situation parameter of a target sound equipment; extracting each single performance score of the basic hardware parameter information, determining the single performance score with the lowest value as a physical short board score, and calculating sample variances between the single performance scores to represent hardware architecture imbalance; the application aims to solve the problem of how to overcome the bottom-layer computing power evaluation distortion of heterogeneous sound equipment caused by hardware architecture imbalance and dynamic working condition fluctuation in the prior art, and the problem of pipeline rupture and display memory deadlock easily caused when performing unstructured special effect script and multimedia stream differential update in a resource-limited environment, so that precise resource adaptation and safe loading with high robustness and zero crash can be realized under the strict physical red line constraint.

Description

Technical Field

[0001] This invention relates to the technical field of digital multimedia, and specifically to a method, system, and device for resource adaptation of an entertainment platform. Background Technology

[0002] With the popularization of intelligent digital entertainment platforms, heterogeneous audio devices with different chip architectures, memory capacities, and rendering capabilities are being connected to these platforms. Traditional streaming media resource scheduling and adaptation mechanisms are gradually revealing their limitations at the physical logic level. Existing resource distribution systems typically rely solely on reading the device's static model tag or using a linear weighted summation method to evaluate terminal computing power. This evaluation mechanism is easily masked by high-end hardware (such as a high processor clock speed) that masks underlying shortcomings such as insufficient storage bandwidth or memory capacity—a "weakest link" in the hardware hierarchy. This leads to physical clock inefficiencies and bus congestion when the platform sends high-definition audio, video, or visual effects streams exceeding its actual capacity, due to the unbalanced hardware architecture. Furthermore, existing solutions often overlook the decrease in available computing power caused by dynamic environmental fluctuations such as temperature-controlled frequency throttling and background process occupancy during long-term, high-load operation. Furthermore, when switching multimedia resources or updating special effects, the industry-standard "global large file overwrite" strategy not only causes concurrent network congestion in the cloud but also triggers I / O blocking on non-volatile storage media at the device side. Especially when dealing with unstructured visual effects shader scripts, the lack of differential distribution with underlying semantic connectivity verification can easily lead to input dependency breaks in the graphics rendering pipeline, causing the graphics processor to interrupt compilation during runtime. In addition, the underlying file system lacks atomic transaction rollback defenses against sudden power outages during hot updates, and lacks dynamic defragmentation and spinlock control capabilities to handle memory fragmentation caused by resource switching. This makes target terminal devices prone to hardware-level failures such as screen flickering, memory overflow deadlocks, and audio underflow stuttering under complex operating conditions, resulting in insufficient stability of the entire platform's resource scheduling system. Summary of the Invention

[0003] This invention provides a method, system, and device for resource adaptation on an entertainment platform. The purpose is to solve the problems in the prior art of how to overcome the distortion of underlying computing power assessment caused by the unbalanced hardware architecture and dynamic operating condition fluctuations of heterogeneous audio equipment, as well as the pipeline breakage and memory deadlock that are easily caused when performing differential updates of unstructured special effects scripts and multimedia streams in resource-constrained environments. In this way, it can achieve highly robust, zero-crash, accurate resource adaptation and safe loading under strict physical red line constraints.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A method for resource adaptation on an entertainment platform includes: acquiring basic hardware parameter information and underlying physical state parameters of a target audio device; extracting individual performance scores from the basic hardware parameter information, identifying the lowest individual performance score as the physical bottleneck score, and calculating the sample variance between the individual performance scores to characterize the hardware architecture imbalance; performing nonlinear exponential penalty calculation based on the physical bottleneck score and the sample variance to obtain a basic bottleneck computing power index; and performing multidimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters to generate multidimensional physical constraints. Elasticity status index; the multi-dimensional physical constraint elasticity status index is concatenated with the resource consumption feature vector of the target song to form a joint feature vector, which is input into a pre-trained reinforcement learning adaptation engine. The reinforcement learning adaptation engine outputs the target resource adaptation strategy through forward propagation inference; the media track specifications defined by the target resource adaptation strategy are parsed, and bidirectional track differential verification is performed according to the media track specifications currently loaded on the target audio device. Based on the verification results, missing incremental track data blocks are extracted from the multi-level resource pool, and the incremental track data blocks are pushed to the target audio device for timestamp alignment and physical composite loading.

[0005] In one aspect of the present invention, obtaining the basic hardware parameter information of the target audio device includes: To detect the underlying kernel architecture of the current operating system; When running in a microkernel operating system environment, it requests an anonymous shared memory region from the memory management unit and maps it to the virtual address space of the hardware monitoring process through the inter-process communication protocol; Based on the memory barrier instructions of the microkernel architecture, and according to the preset double-word alignment rules and the actual physical byte length occupied by each static parameter, the absolute memory read pointer address of each static parameter in the shared memory region is calculated recursively. Based on the absolute memory read pointer address, non-blocking data reading is performed in the virtual address space to obtain the basic hardware parameter information.

[0006] In one aspect of the present invention, the step of performing multi-dimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters of the hardware to generate a multi-dimensional physical constraint elastic state index includes: A sliding historical window with multiple consecutive sampling periods extending forward is constructed, and a differentiated physical time decay weight coefficient is applied to the real-time collected hardware underlying physical state parameters to perform time series weighted moving average filtering. The filtered situation parameters are uniformly converted into dynamic availability performance sub-scores through normalization mapping, and then physical weighted summation is performed to obtain the overall dynamic availability performance score. Obtain the first weighting coefficient and the second weighting coefficient, use the first weighting coefficient to constrain the basic bottleneck computing power index, use the second weighting coefficient to constrain the dynamic availability performance overall score, and add the two together to generate the multidimensional physical constraint elasticity status index.

[0007] In one aspect of the invention, the reinforcement learning adaptation engine uses an objective physical reward function to update network parameters during the offline training phase, and the calculation process of the objective physical reward function includes: At the decision cut-off point where media segment playback ends, obtain the graphics processor's hard frame drop rate, the cumulative number of data underflow interrupts in the audio hardware driver buffer, and the media decoder efficiency score obtained based on the segment mapping of average single-frame video hard decoding time. The absolute difference between the multidimensional physical constraint elasticity state index and the mean value of each physical orbit level actually extracted by the current execution strategy is calculated to quantify the degree of mismatch between resources and capabilities, and then the hardware resource utilization efficiency scalar is derived. An objective deterministic physical reward value is generated by comprehensively considering the hard frame drop rate, the cumulative number of data underflow interruptions, the media decoder efficiency score, and the hardware resource utilization efficiency scalar through a preset weight allocation system, and the objective deterministic physical reward value is used for gradient backpropagation.

[0008] In one aspect of the invention, the incremental orbit data block includes an unstructured visual effects script, and the process of physically compounding the unstructured visual effects script includes: The target visual effects script and the current local visual effects script are parsed into the target abstract syntax tree and the current abstract syntax tree, respectively. Extract node execution features and generate high-order hash values; extract preliminary incremental difference node sets through set intersection comparison and difference operation. Traverse the input pins of each node in the initial incremental differential node set. When it is determined that there is a parent node that is not included in the initial incremental differential node set and is not included in the local live node hash table, trigger a rendering pipeline semantic break alarm. Reverse physical tracing is performed along the topological path of the target abstract syntax tree, and all missing parent nodes with dependency bearing relationships are forcibly added into the incremental set to form the final incremental differential node set of the physical closed loop. The final incremental differential node set is reorganized into a micro incremental syntax tree and deserialized into a physical byte stream text, which is then dynamically compiled and linked to the shader execution queue of the target audio device at runtime.

[0009] In one aspect of the present invention, during the process of pushing the incremental track data block to the target audio device for loading, a two-phase commit and hash verification mechanism is used to ensure data transaction integrity, specifically including: During the preparation phase, a temporary physical sector in a logically isolated state is requested from the operating system's file system. All received incremental track data blocks are sequentially written into the temporary physical sector, and cyclic redundancy check codes are calculated in blocks. During the verification and submission phase, the complete bit stream of the temporary physical sector data is read to calculate the overall physical integrity hash value, and the overall physical integrity hash value is compared with the theoretical target integrity hash value carried by the issued task to determine the physical bit-level equivalence. If the determination result is true, the original resource file pointer is deleted through an atomic renaming operation, and the pointer of the temporary physical sector is mounted to the standard resource directory tree to complete the commit; if the determination result is false or the system is in a sudden power outage and restart state, a forced jump to the rollback stage is performed, and a hardware erase operation is performed on all sector bits of the temporary physical sector at the physical addressing level, and the pointer is anchored to the absolutely complete old version baseline resource file.

[0010] In one aspect of the present invention, during the process of pushing the incremental track data block to the target audio device for loading, dynamic defragmentation of the rendering pipeline memory based on physical time slices is triggered, specifically including: Scan the physical address mapping table of the video random access memory, calculate the ratio of the largest contiguous free physical memory segment to the sum of all free physical memory blocks, and derive the dimensionless video memory fragmentation physical index. When the dimensionless memory fragmentation physical index exceeds the set threshold, calculate the physical time consumed by the pre-rendering task in the current rendering frame, and deduce the remaining safe physical time window of the current frame by combining the hard physical cycle benchmark and the safety redundancy margin. Based on the physical read / write bandwidth of the graphics processor's memory bus, calculate the maximum byte size of the surviving texture block that can be safely moved within the safe physical time window; Select surviving texture resource blocks that meet the maximum byte size limit and physically copy them from the low address space to a contiguous high address space; By comparing and swapping spinlocks at the hardware instruction level, under the unified virtual memory management mechanism, it is detected whether the unified virtual memory pointer strictly points to the old physical base address before the relocation. Only when the detection passes, the unified virtual memory pointer is overwritten and redirected to the new physical base address after the relocation within an indivisible physical clock cycle.

[0011] In one aspect of the invention, the reinforcement learning adaptation engine further integrates priority experience replay control logic during the offline training phase, specifically including: The joint feature vector at the decision moment, the target resource adaptation strategy executed, the objective deterministic physical reward value generated by the settlement, and the next state are encapsulated into a physical decision quadruple state vector and pushed into the experience replay buffer. Calculate the absolute value of the time difference error for each quadruple sample, so that the probability of the quadruple sample being selected is positively correlated with the absolute value of its corresponding time difference error, thereby forcibly increasing the selection frequency of samples at the severe physical working conditions boundary. Within a preset training step size, samples are randomly selected in batches according to the sampling probability and transferred to the optimizer for network parameter updates, and hard synchronization overlay of the target network parameters is performed periodically.

[0012] In another aspect, the present invention also relates to a resource adaptation system for an entertainment platform, used to perform the above-described method for resource adaptation of an entertainment platform, the system comprising: The device parameter acquisition module is used to acquire basic hardware parameter information and underlying physical status parameters of the target audio device; The performance evaluation module is used to extract the individual performance scores of the basic hardware parameter information, determine the lowest individual performance score as the physical bottleneck score, calculate the sample variance between the individual performance scores, perform nonlinear exponential penalty calculation to obtain the basic bottleneck computing power index, and perform multidimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters of the hardware to generate a multidimensional physical constraint elastic state index. A reinforcement learning adaptation engine is used to combine the multidimensional physical constraint elasticity status index with the resource consumption feature vector of the target song, and output the target resource adaptation strategy through forward propagation inference. The resource scheduling module is used to parse media track specifications and perform bidirectional track differential verification. It extracts missing incremental track data blocks from the multi-level resource pool and pushes them to the target audio device for timestamp alignment and physical composite loading.

[0013] In another aspect, the present invention also relates to an electronic device comprising: The system includes an internally connected non-volatile memory, a multi-core central processing unit, a random access memory, a graphics processing unit, and a network physical communication module. The non-volatile memory persistently stores a set of machine code instructions. After the device is powered on and reset, the multi-core central processing unit loads the set of machine code instructions into the random access memory for decoding and execution, thereby realizing the resource adaptation method of the entertainment platform described above.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention reconstructs the multimedia resource adaptation system through five mutually synergistic steps, resulting in the following beneficial effects: S1's multi-dimensional performance awareness system overcomes the isolation limitations of the operating system microkernel. By acquiring dynamic and static parameters through shared memory mapping, it reduces evaluation errors caused by device model spoofing and superficial configuration, providing a reliable physical benchmark for resource scheduling. S2 introduces a bottleneck locking and unbalanced variance penalty mechanism, improving the performance evaluation method that easily masks the disadvantages of underlying storage or memory through traditional linear weighting. It quantifies the risk of computing power misalignment between heterogeneous hardware and avoids bus deadlock caused by excessive resource allocation due to local over-configuration. S3 fuses the static physical baseline with dynamic load through time-series weighted filtering and spatiotemporal synthesis, effectively filtering instantaneous system jitter noise and objectively characterizing the elastic computing power boundary of the device under specific heat dissipation conditions. S4 relies on a reinforcement learning adaptation engine to perform nonlinear mapping between elastic situation indicators and media features. Combined with the objective physical reward function of underlying hard frame drops and underflow interrupts, it guides the scheduling strategy to improve hardware resource utilization without crossing performance boundaries, avoiding computing power waste and overload shutdowns. The bidirectional orbital differential verification and composite loading mechanism proposed by S5 utilizes the semantic source tracing and completion of directed acyclic graphs to avoid the risk of compilation breakage during unstructured special effects updates. At the same time, it is supplemented by a two-stage atomic transaction hash rollback and a time-slice-based spin-lock memory dynamic reorganization mechanism. While reducing the downlink bandwidth of the cloud and the read and write pressure of the terminal storage, it improves the operational stability of the device under complex physical conditions such as sudden power outages or high-frequency strategy switching, and provides stable scheduling performance for digital entertainment platforms in heterogeneous concurrent scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a method for resource adaptation on an entertainment platform according to the present invention.

[0017] Figure 2 This is a flowchart illustrating step 1 of a resource adaptation method for an entertainment platform according to the present invention.

[0018] Figure 3 This is a flowchart illustrating step 2 of the resource adaptation method for an entertainment platform according to the present invention.

[0019] Figure 4 This is a flowchart illustrating step 3 of a resource adaptation method for an entertainment platform according to the present invention.

[0020] Figure 5 This is a flowchart illustrating step 4 of a resource adaptation method for an entertainment platform according to the present invention.

[0021] Figure 6 This is a flowchart illustrating step 5 of a resource adaptation method for an entertainment platform according to the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to embodiments. These embodiments are merely some, not all, of the embodiments of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the protection scope of the present invention.

[0023] Please see Figure 1 As shown, this embodiment discloses a method for resource adaptation of an entertainment platform. In one implementation, the method includes: S1. Obtain the basic hardware parameter information and underlying physical state parameters of the target audio device; S2. Extract the individual performance scores of the basic hardware parameter information, determine the individual performance score with the lowest value as the physical bottleneck score, and calculate the sample variance between the individual performance scores to characterize the unbalance of the hardware architecture. S3. Based on the physical bottleneck score and the sample variance, perform nonlinear exponential penalty calculation to obtain the basic bottleneck computing power index, and perform multidimensional spatial transformation and spatiotemporal synthesis on the basic bottleneck computing power index and the underlying physical situation parameters of the hardware to generate a multidimensional physical constraint elastic situation index. S4. The multidimensional physical constraint elasticity state index and the resource consumption feature vector of the target song are concatenated into a joint feature vector, which is then input into a pre-trained reinforcement learning adaptation engine. The reinforcement learning adaptation engine outputs the target resource adaptation strategy through forward propagation reasoning. S5. Parse the media track specifications defined by the target resource adaptation strategy, perform bidirectional track differential verification based on the media track specifications currently loaded on the target audio device, extract the missing incremental track data blocks from the multi-level resource pool based on the verification results, and push the incremental track data blocks to the target audio device for timestamp alignment and physical composite loading.

[0024] In this embodiment, it should be noted that in S1, during the high-concurrency distribution stage of multimedia content on the entertainment platform, in order to solve the technical problem of the underlying performance perception deviation caused by the reliance on static model mapping when heterogeneous terminals are accessed by traditional systems, a multi-dimensional device performance perception system covering static boundaries and dynamic working conditions is constructed through S1.

[0025] Specifically, by utilizing microkernel memory mapping and system environment detection threads, the static physical limits of the target audio device's computing, storage, rendering, and communication are determined at system access time. Simultaneously, a background periodic sampling mechanism acquires real-time load and transmission rate induced by factors such as temperature-controlled frequency reduction and process congestion. This data processing logic changes the existing recommendation methods' reliance on macroscopic device labels for fuzzy classification, transforming fragmented hardware appearances into low-level operating parameters with physical causal attributes. It solves the evaluation error problem caused by the lag in heterogeneous environment perception, eliminates initial interference caused by device model spoofing or inflated apparent configurations, and achieves a unified benchmark for the target audio device's physical capabilities in both static and dynamic dimensions. This provides structured data support for subsequent extraction of bottlenecks and computational power mapping.

[0026] It should be noted that the dynamic sensing period is determined based on historical test results of the underlying operating system context switching overhead and performance degradation sensitivity. Specifically, during the R&D phase, bursts of concurrent threads were injected into audio environments with various chip architectures, and the time distribution curves of their stable reflection in the kernel monitoring socket were statistically analyzed. For example, experiments showed that when the sampling window is less than 100 milliseconds, due to frequent kernel preemption, the system scheduling overhead will increase sharply to more than 20%, making monitoring itself a performance bottleneck. When the window is greater than 3 seconds, the real-time performance of physical situation features is significantly delayed, making it impossible to capture occasional hard frame drops and hot frequency reduction in time. By fitting the normal distribution of tens of thousands of performance degradation logs, taking the balance point covered by its 95% confidence interval and combining it with redundancy processing, the sampling period of dynamic physical situation was finally calibrated offline to a constant 1 second, which satisfies both the high synchronization rate of situation data and reflects extremely low system detection overhead.

[0027] In S2, in response to the problem that the performance of various hardware components in heterogeneous multimedia devices is extremely uneven, which makes it easy for conventional linear weighted scoring to be masked by a single high-end component, thus causing playback stuttering and resource crashes, S2 introduces a bottleneck locking mechanism based on the barrel effect and variance penalty.

[0028] Specifically, by constructing an objective benchmark reference coordinate system, the actual measured values ​​of various hardware components are segmented and normalized into standard scalars. Then, abandoning weighted summation, the lowest score is directly extracted as the physical weakness score through minimum value retrieval, and the variance of the full-dimensional samples is calculated. This calculation logic establishes an algebraic mapping between the physical limits of the overall system computing power and the imbalance between various hardware indicators, solving the technical problem of performance misjudgment caused by high-spec processors masking low-spec memory. It assigns reasonable penalty weights to the unbalanced architecture, avoiding the trap of high-score, low-performance computing power assessment during resource scheduling.

[0029] In S3, after solving the problem of distortion in static hardware parameter evaluation, in order to overcome the problem that the device still receives excessive media streams when it is fully loaded or overheated and reduces frequency due to relying solely on static computing power distribution, S3 combines runtime situation to synthesize a multi-dimensional physical constraint elastic situation index.

[0030] Specifically, a weighted moving average filter is applied to the underlying physical parameters of the hardware, transforming the filtered indicators into a unified available performance score. Finally, using preset first and second weighting coefficients, the basic bottleneck computing power index and the overall dynamic available performance score are spatiotemporally fused. This judgment logic deeply correlates the inherent physical characteristics of the device with the acquired environmental load, resolving the issue of memory overflow or pipeline deadlock passively caused by ignoring dynamic frequency reduction during heavy rendering operations. It also filters out indicator jumps caused solely by instantaneous background system jitter, improving the accuracy of overall available computing power verification.

[0031] After confirming the characteristics of the terminal's absolute elastic computing power, it is necessary to transform it and the high-dimensional nonlinear media overhead into a precise basis for adaptation decisions in order to solve the technical problem of misaligned distribution of audio, video and special effects tracks in entertainment platforms.

[0032] Specifically, by extracting the discretized consumption features of the target song in terms of audio, video, and visual effects, these features are concatenated with the aforementioned elasticity indicators to form a joint feature vector. This vector is then input into a reinforcement learning adaptation engine driven by a deep Q-network. The engine selects a discretized resource adaptation strategy with technical differences through forward propagation inference. This operational logic accurately maps the rigorously verified physical capability boundaries to resource supply strategies. Combined with the multi-dimensional decoupling characteristics of the media itself, it provides quantitative conditions for subsequently issuing streaming media retrieval commands that do not exceed the hardware physical limits, preventing system failures caused by directly issuing the full amount of ultra-high-definition resources.

[0033] In S5, to address the problem that traditional streaming media distribution uses global file overwrite to force the system to waste bandwidth and cause severe storage I / O blocking, S5 implements multi-level bidirectional track differential verification and physical composite loading.

[0034] Specifically, it blocks full download commands that would cause congestion, compares the target strategy with the media track specifications currently resident on the device, extracts only the missing incremental layer data blocks from the resource pool, and performs timestamp alignment and physical composite loading at the decoder front end. This mechanism solves the problem of global image quality degradation caused by simply reducing global resolution to save bandwidth. It abandons the inefficient repeated erasing strategy and instead uses differential verification with layer decoupling for gap-filling incremental transmission. While ensuring the basic goal of playback continuity, it releases bus resources of non-volatile storage media and improves the efficiency of global network communication from the cloud to the end in high-concurrency distribution scenarios.

[0035] In summary, the resource adaptation method of the entire entertainment platform firstly achieves unified acquisition of static baselines and dynamic fluctuations of heterogeneous devices through the multi-dimensional physical situational awareness mechanism of S1, filtering out interference from environmental model spoofing. Furthermore, S2 introduces a physical bottleneck and variance penalty calculation model, transforming hardware imbalance defects into physical attenuation parameters, giving performance evaluation a reasonable nonlinear tolerance, and improving the evaluation distortion problem caused by existing weighted smoothing. Further, S3 changes the static allocation method, combining runtime state to synthesize elastic situational indicators, distinguishing between normal system fluctuations and long-term performance bottlenecks, and improving the reliability of physical boundary evaluation. Thirdly, S4 combines physical constraints with media characteristics, using a reinforcement learning engine to conduct high-dimensional game theory, seeking the optimal playback specifications while avoiding resource overflow. Finally, S5 proposes a bidirectional track differential verification mechanism for network and storage-constrained environments, changing the full coverage strategy when improving picture quality, and instead using hierarchical decoupling to extract incremental data blocks for physical composite. This design improves the audiovisual experience while greatly reducing the invalid occupation of communication channels and storage I / O, optimizing the global flow efficiency of the platform's multimedia scheduling.

[0036] like Figure 2 As shown, in one specific embodiment, S1 includes: S11. Detect the underlying kernel architecture of the current operating system.

[0037] S12. When running in a microkernel operating system environment, request an anonymous shared memory region from the memory management unit and map it to the virtual address space of the hardware monitoring process through the inter-process communication protocol.

[0038] S13. Based on the memory barrier instructions of the microkernel architecture, and according to the preset double-word alignment rules and the actual physical byte length occupied by each static parameter, recursively calculate the absolute memory read pointer address of each static parameter in the shared memory region.

[0039] S14. Based on the absolute memory read pointer address, perform non-blocking data reading in the virtual address space to obtain the basic hardware parameter information.

[0040] In this embodiment, it should be noted that in S11, during the device initialization and access phase, to address the issue of missing basic information caused by data read permission restrictions under heterogeneous operating systems, S11 establishes a detection mechanism for the underlying kernel architecture. This mechanism identifies whether the system is in a monolithic kernel or a distributed microkernel environment by calling core system libraries. This computational logic incorporates the previously unpredictable operating environment into a categorized management framework, resolving potential out-of-bounds exceptions that might occur when calling APIs across different layers. This provides a guarantee for the targeted distribution of data collection strategies and ensures secure isolation for subsequent information extraction.

[0041] In S12, after kernel-mode detection is completed, to avoid the failure of regular system calls caused by the strict user-mode isolation under the microkernel architecture, S12 performs the allocation and mapping of anonymous shared memory regions. In the current communication process, specific physical pages are allocated using the memory management unit, and file descriptors are passed through inter-process communication protocols to complete the virtual address space mapping. This mechanism enables the acquisition of data channels that bypass the microkernel's overloaded message passing mechanism. This step resolves the performance awareness blind spot caused by system service isolation in the microkernel environment, expanding the data interaction perspective from inefficient traps and context switching to zero-copy, high-speed direct memory mapping, providing a physical communication foundation for subsequent high-frequency situational awareness.

[0042] In S13, after acquiring the high-speed data channel, S13 is responsible for calculating the absolute memory read pointer addresses of various hardware parameters in shared memory. Based on a preset 64-bit double-word alignment rule, the actual physical byte length occupied by each static parameter preceding the target parameter is recursively accumulated with the preset number of null pointer padding bytes to obtain the absolute physical memory offset of the current parameter. This derivation logic transforms the operation that originally relied on fuzzy structure parsing into memory pointer addressing with strict physical boundaries, solving the problem of data structure misalignment leading to garbled parsing in cross-process communication. This gives discrete hardware features deterministic location coordinates in the memory pool, providing core addressing support for accurate, non-blocking extraction.

[0043] In S14, after addressing calculation is completed, S14 performs the actual non-blocking data read. Using the calculated absolute memory read pointer address, it directly obtains basic hardware parameters from the virtual address space, such as the CPU's clock speed, total random access memory capacity, available remaining space on non-volatile storage media, graphics processor core specifications, and the maximum bandwidth of the network interface. This step directly performs zero-blocking direct memory access based on the determined memory coordinates, fundamentally eliminating the process scheduling latency caused by traditional system calls and ensuring the absolute timeliness and integrity of physical situation data.

[0044] like Figure 3 As shown, in one specific embodiment, S2 includes: S21. Extract the individual performance scores of the basic hardware parameter information. After obtaining the static hardware parameters, compare them one by one with the preset global universal advanced hardware benchmark reference values ​​and perform segmented normalization calculations.

[0045] S22. Determine the lowest single performance score as the physical weakness score. Iterate through the normalized scores of each item and select the minimum value in the five-dimensional scalar space.

[0046] S23. Calculate the sample variance among the individual performance scores to characterize the hardware architecture imbalance. The sample variance is obtained by using the sum of squared deviations of each individual performance score relative to its arithmetic mean.

[0047] The pre-defined globally applicable advanced hardware benchmark reference values ​​are determined as follows: Based on a limited number of historical multimedia playback stress test data, the baseline of hardware resource consumption of mainstream devices when smoothly parsing ultra-high-definition resources and fully loading 3D effects is collected; through cluster analysis of the extreme values ​​of hardware configurations of one thousand high-performance sample devices, the lower limit of hardware parameters that can fully meet the requirements for smooth playback of top-tier resources is extracted and established as the benchmark reference value. For example, historical stress test data shows that when the random access memory capacity reaches 32 gigabytes and the graphics processor reaches 60 frames per second, the frame drop rate of the device loading top-tier resources is constant at zero. This set of parameter combinations is extracted and fixed as the benchmark reference value for normalization alignment of heterogeneous devices.

[0048] In this embodiment, it should be noted that in S21, in order to transform the extremely complex heterogeneous hardware values ​​at the underlying level into a consistent evaluation benchmark, S21 establishes a unified scalar space by extracting individual performance scores. The five extracted core parameters are traversed and compared with benchmark reference values ​​calibrated based on a cloud database. If the actual value exceeds or equals the benchmark value, it is truncated to a full score of 100; if the actual value is less than the benchmark value, the ratio between the two is calculated and converted into a relative score on a percentage scale. This operational logic transforms discrete hardware indicators with varying physical units into dimensionless standard scalars. It resolves the contradiction that heterogeneous parameters cannot be directly subjected to horizontal algebraic calculations, eliminates the distortion of the evaluation system caused by differences in absolute numerical values, and achieves alignment of various hardware subsystems on a single dimension, providing a unified measurement platform for subsequent bottleneck identification.

[0049] In S22, after unifying the dimensions, to overcome the problem of traditional systems focusing only on overall high-performance indicators while ignoring critical defects, S22 introduces a physical bottleneck locking mechanism. Based on the bottlenecks in the computer architecture, a minimum value retriever directly locks the lowest value from the extracted normalized performance scores and solidifies it as the physical bottleneck score for that device. This logic of extracting minimum values ​​changes the practice of masking hardware disadvantages by simply averaging or linearly adding multiple parameters. It solves the technical problem at its physical root that a blockage in a critical node in the multimedia rendering process can paralyze the entire hardware and software pipeline, giving the system the ability to directly see the core physical bottleneck and avoiding the distribution of media streams exceeding the bottleneck's capacity due to falsely high average scores.

[0050] In S23, after identifying the bottleneck, it's necessary to quantify the performance dispersion among components to address the issue of increased waiting overhead caused by processing rate mismatches between subsystems under high concurrency conditions. S23 generates a quantitative indicator characterizing the unbalance of the hardware architecture by calculating the sample variance between these five normalized performance scores. This physically directly represents the computing power mismatch between the CPU and memory, and between graphics rendering and network throughput. A larger variance indicates a severe problem of high- and low-performance components being mixed within the device. This variance calculation logic not only quantifies single-point bottlenecks but also the discrete danger state of system collaborative efficiency, serving as a key indicator of the risk of communication deadlock within the device and providing core variable inputs for subsequent nonlinear penalty calculations.

[0051] like Figure 4 As shown, in one specific embodiment, S3 includes: S31. Calculate the basic bottleneck computing power index by performing nonlinear exponential penalty calculation based on the physical bottleneck score and the sample variance.

[0052] S32. Construct a sliding historical window that extends forward through multiple consecutive sampling periods, and apply differentiated physical time decay weight coefficients to the real-time acquired hardware underlying physical state parameters to perform time series weighted moving average filtering.

[0053] S33. The various situational parameters after filtering are uniformly converted into dynamic availability performance sub-scores through normalization mapping, and the overall dynamic availability performance score is obtained by physical weighting and calculation.

[0054] S34. Obtain the first weight coefficient and the second weight coefficient, use the first weight coefficient to constrain the basic bottleneck computing power index, use the second weight coefficient to constrain the dynamic availability performance overall score, and add the two together to generate the multi-dimensional physical constraint elasticity status index.

[0055] In this embodiment, it should be noted that, in S31, in order to accurately calculate the actual computing power baseline with physical constraints, the following nonlinear penalty calculation formula is introduced:

[0056] in, This represents the calculated basic bottleneck computing power index, whose value is a dimensionless real number. This represents the physical weakness score among the five extracted hardware performance scores; This represents the exponential function operator with the natural constant e as its base; This represents the system's default hardware architecture imbalance penalty coefficient. This represents the sample variance among the calculated individual performance scores.

[0057] The threshold for the dimensionless memory fragmentation physical index is determined as follows: Based on a limited number of historical concurrent device operation data, during continuous high-frequency strategy switching tests, the critical point data between the memory fragmentation physical index and the system throwing a memory overflow exception is collected. By statistically analyzing the distribution of 5,000 memory overflow logs, the lower bound of the fragmentation index that triggers rendering pipeline deadlock is extracted, and a certain engineering safety redundancy is reserved. For example, historical data statistics show that when the fragmentation index reaches 0.78, the device begins to experience memory allocation failures, and when the index reaches 0.85, a large-scale deadlock occurs. Using this critical point of 0.78 as a benchmark, after deducting the safety redundancy, the final threshold is set at 0.65.

[0058] The calculation of this formula is based on the theory of exponential internal friction decay in non-equilibrium physical systems. Because highly mismatched components (such as a top-tier processor paired with extremely slow storage media) generate significant physical clock idling and blocking, leading to a substantial drop in computing power, the variance is... With penalty coefficient Combined with the negative power term that decays exponentially, it directly affects the absolute weakest link score of the system. This logic completely blocks the traditional weighted summation algorithm, solving the problem of inflated evaluation systems caused by locally high hardware configurations. By exponentially reducing computing power to assess boundaries, it effectively ensures that the subsequent decision engine will not make out-of-bounds adaptations that would cause memory overflow or physical crashes under various hardware configurations.

[0059] In S32, to completely eliminate instantaneous data noise interference caused by occasional system jitter or sudden writes to the operating system's low-level logs at the physical layer, S32 constructs a sliding historical window extending forward through multiple consecutive sampling periods. Specifically, differentiated physical time decay weight coefficients are applied to the real-time acquired hardware-level physical situation parameters, performing time-series weighted moving average filtering. The sampling period closer to the current time is assigned a larger physical time decay weight coefficient. This filtering logic, based on fundamental principles, strictly distinguishes between substantial performance degradation of physical hardware and instantaneous context switching jitter at the software level. It solves the technical problem in traditional solutions where a single spike in CPU utilization could erroneously trigger global image quality degradation, ensuring that the dynamic situation parameters input to the evaluation model possess extremely high physical fidelity and anti-interference capabilities.

[0060] The physical time decay weight coefficient for each historical sampling period is determined as follows: Based on a finite number of historical multimedia playback stress test data, the performance index time-series change curves of the system under different operating conditions are collected; by performing autocorrelation analysis and exponential smoothing on the situational data of 10,000 consecutive sampling periods, the time decay gradient parameter that most sensitively reflects the current performance degradation is extracted. For example, data processing and analysis show that when the decay weights from the current period to the previous four periods are set to 0.40, 0.30, 0.15, 0.10, and 0.05 respectively, the filtering results can eliminate most of the instantaneous system jitter noise and ensure that the response delay to the actual computing power degradation is within a safe range. Therefore, the above physical time decay weight coefficient is solidified based on the results of this finite number of historical data processing.

[0061] In S33, due to the fundamental differences in physical dimensions among the collected underlying hardware physical situation parameters (for example, processor load rate is a negative constraint indicator, with higher values ​​indicating scarcer available resources; while network transmission rate is a positive capability indicator), S33 transforms the filtered situation parameters into dynamic availability performance sub-scores through normalization mapping. Specifically, for negative constraint indicators, a full-score deduction mechanism is used for mapping; for positive capability indicators, the ratio of actual values ​​to benchmark reference values ​​is used. Subsequently, each sub-score is assigned a strictly symmetrical physical allocation coefficient, and a physical weighted sum is calculated to obtain the overall dynamic availability performance score. This calculation logic eliminates the dimensional barriers between multi-source heterogeneous dynamic physical quantities, solves the problem that complex operating conditions during runtime cannot be uniformly measured by a single scalar, and provides standardized dynamic input for the final situation synthesis.

[0062] In S34, a first weighting coefficient and a second weighting coefficient are obtained. The first weighting coefficient is used to constrain the aforementioned basic bottleneck computing power index, and the second weighting coefficient is used to constrain the overall dynamic availability performance score. The two are then added together to generate a multi-dimensional physical constraint elasticity index. Specifically, the first weighting coefficient must be strictly greater than the second weighting coefficient, and the sum of the values ​​of the first and second weighting coefficients must equal 1. This limits the absolute dominance of the inherent physical bottleneck of static hardware in long-term resource adaptation, while using dynamic sudden specific operating conditions as fine-tuning correction factors. This spatiotemporal synthesis logic completely strips away the previous relatively absolute capability evaluation system. It not only depicts the extreme physical boundaries of the target audio equipment under normal temperature and pressure, but also accurately quantifies its elastic contraction range under specific loads and specific heat dissipation conditions, providing the resource adaptation engine with an extremely definite and insurmountable physical red line indicator.

[0063] Specifically, the first and second weighting coefficients are determined as follows: Based on a limited number of historical concurrent device operation data, the accuracy of the system's comprehensive assessment of static computing power limitations and dynamic load fluctuations under different weighting ratios is collected. Through linear regression and grid search optimization on five thousand sets of historical data with different weighting ratios, the weight distribution characteristics that minimize performance assessment errors are extracted. For example, historical data processing results show that when the static first weighting coefficient is 0.6 and the dynamic second weighting coefficient is 0.4, the system has the highest resource adaptation success rate and the most stable model convergence under sudden loads. Therefore, based on these limited historical data processing results, the first weighting coefficient is fixed at 0.6 and the second weighting coefficient at 0.4.

[0064] like Figure 5 As shown, in one specific embodiment, the above-mentioned S4 includes: S41. Obtain the discretized consumption feature scalars of the target song in terms of audio sampling rate, video resolution, visual effects complexity, and total amount of resource files, and perform dimensionless mapping with the multidimensional physical constraint elasticity state index to form a joint feature vector. S42. Input the joint feature vector into the input layer of the deep network of the pre-trained reinforcement learning adaptation engine; S43. The reinforcement learning adaptation engine performs forward propagation inference through multiple fully connected hidden layers. S44. Select the neuron with the largest Q-value estimate in the network output layer and map its corresponding discretized action to the target resource adaptation strategy.

[0065] In this embodiment, it should be noted that after quantifying the physical capabilities of the target audio equipment in S41, the resource consumption on the supply side must also be included in the same high-dimensional space. Specifically, by extracting the physical feature scalars of the target song at the multimedia encapsulation level and concatenating them with the equipment's elasticity status indicators, a joint feature vector is formed that can completely describe the causal relationship between the current equipment operating condition and the current song's pressure. This operational logic provides a complete state-space description for the deep learning model.

[0066] In S42 to S44, traditional rule trees are prone to inefficiency when dealing with highly nonlinear multidimensional state spaces. Specifically, by utilizing the forward propagation mechanism of a reinforcement learning adaptation engine, a high-dimensional nonlinear mapping is performed between the available computing power of the hardware and the consumption requirements of media resources, directly determining the optimal combination strategy at the output layer. This strategy precisely defines the specific extraction specifications of the three physical tracks—audio, video, and special effects—from the multi-level resource pool in the underlying media distribution system. It solves the problems of traditional solutions (such as sacrificing audio to maintain image quality, leading to audio-visual asynchrony) and achieves profit maximization decisions under physical constraints.

[0067] In one specific implementation, to ensure high accuracy of the aforementioned reinforcement learning adaptation engine during forward inference, the reinforcement learning adaptation engine uses an objective physical reward function to update network parameters during the offline training phase, specifically including: S401: At the decision cut-off point at the end of each media segment playback, the graphics processor's hard frame drop rate and the cumulative number of data underflow interrupts in the audio hardware driver buffer are directly obtained through the underlying operating system hook. S402. Obtain the average single-frame video hardware decoding time reported by the multimedia framework, perform segmentation mapping based on the hard physical clock cycle threshold, and obtain the media decoder efficiency score. S403. Calculate the absolute difference between the multidimensional physical constraint elasticity state index and the average value of each physical orbit level actually extracted by the current execution strategy, so as to quantify the degree of mismatch between resources and capabilities, and then derive the hardware resource utilization efficiency scalar. S404. By integrating the hard frame drop rate, the cumulative number of data underflow interruptions, the media decoder efficiency score, and the hardware resource utilization efficiency scalar through a preset weight allocation system, an objective deterministic physical reward value is generated, and the objective deterministic physical reward value is used for gradient backpropagation.

[0068] Specifically, the objectively deterministic physical reward value The specific algorithm model is as follows:

[0069] Furthermore, the hardware resource utilization efficiency scalar The calculation formula is:

[0070] In the above formula, This represents the objective and deterministic physical reward value generated during settlement; This represents the preset objective playback smoothness comprehensive weighting coefficient; , , These represent the sub-allocation coefficients for hard frame drop rate, audio underflow, and decoder efficiency, respectively. This indicates the graphics processor's hardware frame drop rate; This indicates the cumulative number of data underflow interrupts in the audio hardware driver buffer; This indicates the efficiency score of the media decoder; This represents the weighting coefficient for hardware resource utilization efficiency; This represents a scalar measure of hardware resource utilization efficiency. Indicators representing the elastic state of multidimensional physical constraints; This represents the sum of the values ​​of each physical orbital level actually extracted by the current execution strategy; Represents the theoretical maximum limit summation at the physical orbital level; This represents the absolute value operator.

[0071] Specifically, the method for determining the values ​​of the objective playback smoothness comprehensive weight coefficient and its sub-allocation coefficients, as well as the hardware resource utilization efficiency weight coefficient, is as follows: Based on a limited number of historical device reinforcement learning training data, the convergence speed and physical robustness indicators of the deep Q-network under different reward function coefficient ratios are collected; through multi-objective Pareto optimality analysis of log data from two thousand independent training processes, the optimal coefficient solution set that can balance screen smoothness and silicon resource utilization is extracted. For example, historical data verification shows that when the objective playback smoothness comprehensive weight coefficient is set to 0.7, the hardware resource utilization efficiency weight coefficient is set to 0.3, and the sub-allocation coefficients for hard frame drop rate, audio underflow, and decoder efficiency are set to 0.4, 0.4, and 0.2 respectively, the model achieves the highest resource adaptation success rate under severe physical boundaries. Therefore, this set of coefficient configurations is adopted based on the limited number of historical data.

[0072] In this implementation, it should be noted that the reward function of traditional recommendation or scheduling algorithms often relies on fuzzy posterior data such as user click-through rates or subjective ratings. This control chain is extremely long and noisy, making it difficult to use for microsecond-level low-level hardware scheduling. Specifically, in S401 and S402, the reward function is directly rooted at the very bottom of the hardware pipeline. Hard frame drops directly represent the physical exhaustion of the graphics rendering pipeline, data underflow interruptions directly represent the break in bus bandwidth or audio decoding computing power, and decoding time precisely characterizes the computational efficiency of the silicon chip. By constructing the reward function using these extremely objective and measurable low-level physical parameters, the algorithm's dependence on "subjective lag" is completely eliminated.

[0073] Furthermore, in S403 and S404, a hardware resource utilization efficiency scalar is introduced as a crucial penalty. If a high-performance device is allocated extremely low-resolution resources, although there will be no lag (zero frame drop rate), its computing power will be largely idle. The absolute difference between the two will be extremely large, resulting in a negative resource utilization efficiency score, thus imposing a heavy penalty on the global physical reward value. This logic, based on fundamental principles, not only penalizes crashes caused by low-performance overload but also severely punishes the waste of silicon resources caused by high-performance underutilization. Through this absolutely objective and deterministic physical reward function, the neural network is forced to infinitely approach the optimal point of hardware computing power through massive trial and error, thus constructing the core technical solution of this invention.

[0074] like Figure 6 As shown, in one specific embodiment, the above-mentioned S5 includes: S51. The resource scheduling module calls the local cache manager interface to retrieve the media track specification vector currently residing in the random access memory of the target audio device. S52. Parse the media track specification vector defined by the target resource adaptation strategy, compare the two dimension by dimension, and start the bidirectional track differential verification state machine. S53. Based on the verification results, extract incremental orbit data blocks only for missing orbits that are higher than the current specifications at the network end; S54. Push the incremental track data block to the target audio device, and perform time stamp alignment and spatial pixel interpolation physical composite of the base layer and the newly added data block at the front end of the hardware decoder.

[0075] In this embodiment, it should be noted that traditional technologies typically employ a conventional mechanism of interrupting current playback, clearing the cache, and re-downloading the full file of the new specification when dealing with strategy switching. This mechanism not only results in a significant waste of network bandwidth but also causes severe I / O congestion on non-volatile storage media. Specifically, in S51 and S52, through a bidirectional track differential check state machine, the system can accurately perceive the physical data assets currently possessed by the device (e.g., a buffered 480P base video stream). In S53 and S54, utilizing the physical characteristics of stretchable coding, the network end no longer sends the complete 1080P file but only extracts the spatial enhancement layer incremental track data blocks missing from the 480P to 1080P transition. On the device side, the decoder front end forces physical-level clock synchronization, performing pixel-level physical composite interpolation between the newly arrived enhancement layer differential data and the existing base layer data. This logic completely avoids network bandwidth and storage I / O bottlenecks. By replacing macro file overwriting with micro-physical differential compensation, it achieves seamless leaps in resource specifications at the millisecond level, ensuring the playback continuity of audio equipment under drastic fluctuations in operating conditions.

[0076] In one specific implementation, the incremental track data block contains not only stretchable audio and video streams but also unstructured visual effects scripts. Since unstructured shader code or particle scripts cannot be directly modified by subtracting physical volumes, direct slice loading can easily lead to semantic breaks and compilation deadlocks during runtime rendering by the graphics processor. Therefore, the process of physically compounding the unstructured visual effects scripts includes: S501: Perform lexical and syntactic analysis on the target visual effects script retrieved from the cloud resource pool and the current local visual effects script respectively, and parse them into a target abstract syntax tree and a current abstract syntax tree; S502. Traverse all logical execution nodes in the two trees, extract operator type, operand and geometric coordinate variables, and use hash algorithm to generate corresponding high-bit hash value. Then, extract the preliminary incremental difference node set through set intersection comparison and difference operation in discrete mathematics. S503. Start the semantic connectivity verification of the directed acyclic graph. Traverse the input pins of each node in the preliminary incremental differential node set. When it is determined that there is a parent node that is not included in the preliminary incremental differential node set and is not included in the local live node hash table, trigger the semantic break alarm of the rendering pipeline. S504. In response, reverse physical tracing is performed along the topological path of the target abstract syntax tree. A depth-first search algorithm is used to forcibly add all missing parent nodes with dependency bearing relationships into the incremental set, forming the final incremental differential node set of the absolute physical closed loop. S505. The final incremental differential node set is reorganized into a micro incremental syntax tree and deserialized into a physical byte stream text, which is then dynamically compiled and linked to the shader execution queue of the target audio device at runtime.

[0077] In this embodiment, it should be noted that differential updates of unstructured scripts in the graphics pipeline have long been a technical blind spot in the industry. Specifically, S501 and S502 transform unstructured plain text effect code into an abstract syntax tree with strict topological physical boundaries, and achieve node-level physical stripping through hash set operations.

[0078] More importantly, in S503 and S504, the system did not blindly distribute the extracted differential nodes. Based on the principles of graphics rendering, the execution of shader nodes is highly dependent on the preceding lighting or material data (i.e., input pins). If the parent node that a newly added effect node depends on does not exist in the local cache (e.g., it was cleaned up by previous garbage collection), forced compilation will cause the GPU driver compilation to fail. Therefore, a connectivity check of the directed acyclic graph was introduced. By tracing the physical source, all underlying support nodes that cause dependency breaks were forcibly removed and packaged. This extremely rigorous dependency closed-loop logic completely blocks runtime compilation errors caused by differential loading from the physical level, ensuring high implementability and stability of unstructured effects when dynamically injected on the client side.

[0079] In one specific implementation, during the process of pushing the incremental track data block to the target audio device for loading, a two-phase commit and hash verification mechanism is used to ensure data transaction integrity, specifically including: S511. During the preparation phase, a temporary physical sector in a logically isolated state is requested from the operating system's file system. All received incremental track data blocks are sequentially written into the temporary physical sector, and the cyclic redundancy check code is calculated in blocks. S512. During the verification and submission phase, the complete bit stream of the temporary physical sector data is read to calculate the overall physical integrity hash value, and the overall physical integrity hash value is compared with the theoretical target integrity hash value carried by the task to determine the physical bit-level equivalence. S513. If the determination result is true, the original resource file pointer is deleted through atomic renaming operation and the pointer of the temporary physical sector is mounted to the standard resource directory tree to complete the commit; if the determination result is false or the system is in a sudden power outage and restart state, a forced jump to the rollback stage is performed, and a hardware erase operation is performed on all sector bits of the temporary physical sector at the physical addressing level, and the pointer is anchored to the absolutely complete old version baseline resource file.

[0080] In this embodiment, it should be noted that in S511, hot updates of streaming media resources in complex network and unstable power supply environments face the technical risk of file system node corruption due to power outages during write operations. To address this underlying physical risk, a physically isolated temporary sector writing mechanism is introduced in the preparation phase of S511. Specifically, instead of directly overwriting or mounting pointers to the existing baseline multimedia file, the local cache manager requests an absolutely isolated shadow sector from the underlying block device driver. All downlink incremental byte streams received from the network socket are sequentially injected into this physical sector, and a cyclic redundancy check is performed after each fixed small data block injection. This data processing logic changes the dangerous operation mode of downloading and overwriting in existing technologies. It solves the problem of dirty data retention caused by power loss during partial level transitions in flash memory, eliminates overall file structure corruption caused by the loss of a single data packet, achieves physical security isolation between the network stream and the local non-volatile storage medium, and provides independent data sandbox support for subsequent global consistency checks.

[0081] In the S512, to address the issue of memory bit flipping that can occur in incremental data blocks even without packet loss during transmission due to transient interference from the operating system bus, the S512 performs extremely stringent physical bit-level equivalence operations. Specifically, the system's CPU reads the complete binary bit stream residing in the shadow sector, calculates the local overall physical integrity hash value of the batch of data using a one-way hash function, and performs a Boolean equivalence match with the theoretical target hash value encapsulated in the instruction header issued by the cloud. This judgment logic is directly rooted in the principles of information theory, solving the problem of traditional schemes that rely solely on the consistency of file byte size to determine whether the download is complete. It gives the system absolute insight at the physical bit level, preventing system failures caused by individual bit errors leading to a complete deadlock due to illegal instructions encountered by the subsequent hardware decoder.

[0082] In S513, after integrity determination, absolutely atomic state transitions must be implemented at the file descriptor level of the operating system. To address the "read-write tearing" problem that may be encountered during multi-threaded concurrent reading, S513 employs a binary branch logic of atomic renaming operations and physical layer hardware erase rollback. Specifically, when the determination is true, the old pointer is instantly stripped and the new pointer is mounted using the single-clock-cycle atomic instruction of the file system, achieving zero-latency resource upgrades for upper-layer applications. If the determination is false, or if the system detects a dangling shadow sector status bit during the power-on self-test (indicating a previous power outage), it directly penetrates the file system and calls the block erase instruction of the flash controller at the physical addressing level to forcibly clear the electronic state of the temporary sector and hard-roll back the global read-only pointer to the absolutely complete old version of the file before the power outage. This mechanism completely cuts off the physical path of any damaged data flowing into the rendering pipeline, solves the feasibility problem of hot resource updates under extremely harsh device conditions, and establishes a stable and reliable update and rollback mechanism for edge devices by converging all uncertainties into the rollback operation.

[0083] In one specific implementation, during the process of pushing the incremental track data block to the target audio device for loading, dynamic defragmentation of the rendering pipeline memory based on physical time slices is triggered, specifically including: S521. Scan the physical address mapping table of the video random access memory, calculate the ratio of the largest contiguous free physical memory segment to the sum of all free physical memory blocks, and derive the dimensionless video memory fragmentation physical index. S522. When the dimensionless memory fragmentation physical index exceeds the set threshold, calculate the physical time consumed by the pre-rendering task in the current rendering frame, and deduce the remaining safe physical time window of the current frame by combining the hard physical cycle benchmark and the safety redundancy margin. S523. Based on the physical read / write bandwidth of the graphics processor's memory bus, calculate the maximum byte size of the surviving texture block that can be safely moved within the safe physical time window; S524. Select surviving texture resource blocks that meet the maximum byte size limit and physically copy them from the low address space to the contiguous high address space. S525. By comparing and exchanging spinlocks at the hardware instruction level, under the unified virtual memory management mechanism, it is detected whether the unified virtual memory pointer strictly points to the old physical base address before the relocation. Only when the detection passes, the unified virtual memory pointer is overwritten and redirected to the new physical base address after the relocation within an indivisible physical clock cycle.

[0084] In this embodiment, it should be noted that in S521, the reinforcement learning engine will frequently issue resource degradation or effect unloading instructions when the operating conditions deteriorate. This high-frequency operation will cause the graphics processor's memory pool to be fragmented at the physical level. In order to solve the serious problem of "sufficient total memory but inability to allocate large contiguous blocks of memory" (i.e., memory fragmentation OOM) caused by this, a physical quantification index of the fragmentation state is constructed through S521.

[0085] To clearly and unambiguously define the physical extent of memory fragmentation, the system introduces a mathematical model for a dimensionless memory fragmentation physical index, the specific formula of which is as follows:

[0086] In the above formula, This represents the calculated dimensionless memory fragmentation physics index, whose value strictly converges to between 0 and 1. This represents the length in bytes of the largest contiguous free physical memory segment in the current video random access memory, as determined by scanning the physical address mapping table; the unit is megabytes. This represents the arithmetic sum of the lengths of all free physical memory blocks in the current video random access memory, expressed in megabytes.

[0087] This quantitative logic reveals the degree of fragmentation in the system's current memory space (the closer the exponent is to 1, the more fragmented the space). It changes the superficial approach of merely monitoring "remaining memory," eliminates the illusion of security caused by apparent ample memory, and achieves accurate early warning of the risk of underlying video memory allocation failure.

[0088] In S522, when the system detects that the memory fragmentation index exceeds a dangerous threshold (e.g., 0.65), garbage collection must be initiated immediately. However, this raises another serious technical problem: performing large-scale memory copies in the real-time rendering pipeline can directly block the pipeline, causing severe screen stuttering. To resolve the "mutual conflict between defragmentation and real-time rendering," S522 introduces a time-slice amortization mechanism.

[0089] Specifically, the system precisely measures the physical time consumed by the graphics processor in the current frame (e.g., a frame based on a 60Hz refresh rate) to complete the core polygon rendering and pixel shading, and deducts a mandatory safety redundancy margin that is absolutely not allowed to be encroached upon, thereby calculating the limit time window left for the video memory relocation task.

[0090] Its mathematical derivation formula is defined as follows:

[0091] In the above formula, This indicates the maximum safe time allowed for physical memory relocation in the current rendering frame, in milliseconds. The hard physical cycle benchmark set for the system based on a refresh rate of 60 frames per second, in milliseconds; This indicates the actual physical time consumed by the preceding core rendering tasks within the current rendering frame, in milliseconds. This represents the physical safety redundancy margin that the system is rigidly required to retain, used to absorb sudden bus delays.

[0092] For physical safety redundancy margin The method for determining this value is as follows: Based on a limited number of historical rendering pipeline time data, the burst latency and clock jitter of the graphics processor executing memory bus data copying under different loads are collected; by statistically analyzing the memory move instruction time of 10,000 rendering frames, the maximum peak-to-valley difference of its time fluctuation is extracted. For example, actual test data shows that under most operating conditions, the additional transmission latency fluctuation caused by bus preemption or underlying hardware interruption does not exceed 1.8 milliseconds. To ensure that the memory reorganization time will not exceed the single-frame rendering red line even under clock jitter, it is rounded up, and the physical safety redundancy margin is finally determined. It is 2.0 milliseconds.

[0093] This time-constrained logic directly eliminates the uncontrollability of macro-level scheduling, precisely controlling it within the sub-millisecond scale of the physical hardware clock. It solves the inevitable stuttering problem of fragmentation and establishes a strict boundary for performing underlying physical maintenance "in between" without disrupting the continuity of real-time audiovisual output.

[0094] In S523 and S524, after establishing a safe physical time window, the system must strictly calculate the upper limit of data transfer that the time window can handle. S523 extracts the actual physical read / write bandwidth of the motherboard's PCIe bus or the device's internal video memory controller, multiplies it by the calculated safe time window, and obtains the absolutely safe upper limit of bytes to be moved within the current time slice. S524 then selects target textures smaller than this upper limit from discrete small surviving texture blocks, and uses the graphics processor's high-speed direct memory access (DMA) engine to silently copy them from fragmented low-end addresses to consecutive high-end free addresses in the background. This mechanism completely eliminates frame timeouts and frame drops caused by excessive copying, spreading the massive video memory merging project across hundreds or thousands of consecutive physical rendering frames in an "ant-like" manner.

[0095] In the S525, after the physical copy of texture data is completed, the application's virtual pointer still points to the old low-end physical address. If the rendering thread happens to initiate a sampling instruction for the texture at the moment the pointer is redirected, it will cause read-write conflicts and screen tearing errors. To solve this dangling pointer exception under multi-threaded concurrency, the S525 forcibly introduces a hardware instruction-level "Compare-and-Swap (CAS)" spinlock at the underlying level. Specifically, the system issues an atomic instruction in the unified virtual memory controller. Within an indivisible physical clock cycle locked by the bus, this instruction first compares whether the current virtual pointer is still strictly equal to the old physical base address. Only if this condition is absolutely true will the hardware overwrite the virtual pointer with the newly allocated high-end contiguous physical base address. This logic cuts off the race condition in a multi-threaded environment at the silicon instruction level, solves the key parallel safety problem of runtime dynamic memory reorganization, and provides a reliable underlying guarantee for the long-term stable operation of devices under high-frequency resource adaptation.

[0096] In one specific implementation, the reinforcement learning adaptation engine described above is not only directly constrained by the multidimensional physical situation during the online inference stage, but also, during its offline training stage, to prevent the deep neural network from overfitting to "steady-state good conditions" and losing its ability to cope with "marginal harsh conditions," the reinforcement learning adaptation engine also integrates priority experience replay control logic, specifically including: S411. The joint feature vector at the decision moment, the target resource adaptation strategy executed, the objective deterministic physical reward value generated by the settlement, and the next state are encapsulated into a physical decision quadruple state vector and pushed into the experience replay buffer. S412. Calculate the absolute value of the time difference error for each quadruple sample, so that the probability of the quadruple sample being selected is positively correlated with the absolute value of its corresponding time difference error, so as to forcibly increase the selection frequency of samples with severe physical conditions. S413. Within the preset training step size, randomly select transfer samples in batches according to the sampling probability and send them to the optimizer for network parameter updates, and periodically perform hard synchronization coverage of the target network parameters.

[0097] The preset training step size is determined as follows: based on a finite number of historical reinforcement learning training iterations, the network parameter update variance and target policy reward convergence curves are collected under different training step sizes. Through smoothing and calculus extreme point analysis of more than 500 rounds of offline training data, the iteration cycle that minimizes model parameter oscillations and maximizes learning efficiency is extracted. For example, a series of processing steps on historical log data revealed that when the training step size is set to 1,000 iterations, the Q-value estimation error of the target network can steadily decrease without forgetting. Therefore, based on this finite number of historical data, the preset training step size is determined to be 1,000 parameter update cycles.

[0098] In this embodiment, it should be noted that in S411, due to the strong temporal correlation between states during training, directly using continuously generated data streams for backpropagation in deep Q-networks would cause severe oscillations and divergence in parameter updates. To break this temporal correlation, S411 introduces an experience replay buffer. Specifically, the starting environment state of each complete decision cycle, the selected policy action, the objective reward value calculated based on underlying hard metrics, and the resulting new environment state are strongly correlated into an indivisible physical decision quadruple, and continuously pushed into a fixed-capacity circular memory buffer. This data stream management logic provides the model with a giant sandbox for offline observation of historical interactions, solving the problem of loss of generalization ability caused by the high correlation of online reinforcement learning samples.

[0099] In S412, traditional experience replay employs a uniform random sampling strategy, resulting in the model spending most of its time repeatedly learning ineffective safety samples lacking information, such as "normal temperature, low load, and low image quality." When encountering extreme physical conditions like sudden overheating and frequency throttling, the scarcity of such experience leads to unreasonable dimensionality-increasing scheduling. To address this data distribution imbalance, S412 introduces a priority mapping mechanism based on the absolute value of temporal difference error. Specifically, the system calculates the difference between the current network prediction value and the actual reward value for each sample (i.e., temporal difference error). The larger the absolute value of this error, the larger the model's cognitive blind spot under that specific condition, and the corresponding physical state is often extremely close to the device's collapse threshold. The system forcibly constructs a positive correlation function between the absolute value of this error and the sampling probability, causing samples that lead to network prediction failures and are at extremely low limits to be forcibly fed into the neural network for punitive learning at extremely high frequencies. This logic forces artificial intelligence to completely step out of its comfort zone and directly confront the most stringent physical limits, fundamentally improving the model's boundary control capabilities when the device faces physical collapse.

[0100] In S413, during the network parameter update phase, the system strictly follows a sample pool with non-linear extraction probabilities, performing gradient backpropagation in batches. Simultaneously, to address the problem of chasing a moving target caused by frequent changes in the target's Q-value, S413 employs a strict hard synchronization coverage mechanism, meaning that the parameters of the evaluation network are only physically copied to the target network at fixed training steps. This control logic, while maintaining a keen ability to learn under harsh conditions, endows the model with long-term convergence stability, constructing an intelligent control center that can both keenly perceive physical limits and smoothly output adaptive strategies.

[0101] The present invention also provides a resource adaptation system for an entertainment platform. In one embodiment, the system includes: The device parameter acquisition module is used to acquire basic hardware parameter information and underlying physical status parameters of the target audio device; The performance evaluation module is used to extract the individual performance scores of the basic hardware parameter information, determine the lowest individual performance score as the physical bottleneck score, calculate the sample variance between the individual performance scores, perform nonlinear exponential penalty calculation to obtain the basic bottleneck computing power index, and perform multidimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters of the hardware to generate a multidimensional physical constraint elastic state index. A reinforcement learning adaptation engine is used to combine the multidimensional physical constraint elasticity status index with the resource consumption feature vector of the target song, and output the target resource adaptation strategy through forward propagation inference. The resource scheduling module is used to parse media track specifications and perform bidirectional track differential verification. It extracts missing incremental track data blocks from the multi-level resource pool and pushes them to the target audio device for timestamp alignment and physical composite loading.

[0102] In this implementation, it's important to note that the system's architecture completely abandons the tightly coupled monolithic program model. The device parameter acquisition module establishes a physical connection with hardware probes directly at the lowest level through sockets and microkernel interfaces, acting as the system's sensory tentacles extending into the hardware world. The performance evaluation module receives the raw data and performs pure algebraic dimensionality reduction and nonlinear mapping, acting as a mathematical translator of physical laws. The reinforcement learning adaptation engine is independently encapsulated within a computing container with matrix operation acceleration capabilities, serving as the sole neural network brain with decision-making power. The resource scheduling module is located at the network I / O layer and memory control layer, responsible for extremely complex execution logic such as differential comparison, hash verification, atomic commit, and time slice amortization. The physical modules communicate with absolute decoupling through high-speed message queues and zero-copy memory mapping, ensuring that even if a specific module (such as network distribution experiencing blockage) encounters a physical bottleneck, it will not trigger a system-wide interruption. This system topology, strictly adhering to the principles of single responsibility and physical isolation, directly guarantees absolute robustness in large-scale, high-concurrency entertainment platform distribution scenarios.

[0103] The present invention also provides an electronic device, in one embodiment of which includes a non-volatile memory, a multi-core central processing unit, a random access memory, a graphics processing unit, and a network physical communication module with internal communication connections. The non-volatile memory persistently stores a set of machine code instructions. After the device is powered on and reset, the multi-core central processing unit loads the set of machine code instructions into the random access memory for decoding and execution, so as to realize the aforementioned resource adaptation method of the entertainment platform.

[0104] In this embodiment, it should be noted that the hardware bus connection architecture inside the electronic device determines the physical basis for the implementation of the method. The multi-core CPU establishes a direct channel with the random access memory via a high-speed front-side bus to obtain the compiled resource-adaptive system instruction set. During runtime, it senses its own temperature sensor data and interrupt request frequency in real time by calling the Universal Serial Bus (USB) controller or directly operating the I / O ports to provide feedback on the underlying physical status. The graphics processing unit (GPU) does not simply act as a screen output channel; its built-in high-speed memory controller deeply participates in the "dynamic defragmentation of memory based on physical time slices" stage of this invention and receives CAS spinlock control instructions from the CPU. The network physical communication module (such as a gigabit Ethernet physical layer chip or a high-speed RF baseband) acts as the physical channel for interaction with the multi-level resource pool in the cloud, carrying the transmission of bidirectional track differential data streams and the interaction of hash check packets.

[0105] In one specific embodiment, to further utilize objective data to support the prominent substantive features and significant progress of the present invention, this embodiment provides verification data from specific industrial applications under extreme physical conditions. The test scenario targets high-end audio equipment operating under dynamic fluctuations. In this embodiment, it should be noted that traditional scheduling algorithms, when dealing with high-end hardware, typically pre-deploy the highest-specification media stream, completely ignoring potential heat dissipation throttling or background system updates that may occur during operation. This often leads to unexpected and severe stuttering in high-end equipment under specific conditions. Specifically, the physical environment is set as home theater-grade professional audio equipment with extremely high static physical boundaries: a central processing unit (CPU) clock speed of 4.0 GHz, 32 gigabytes of random access memory (RAM), a non-volatile media read rate of 3500 megabytes per second, a graphics processor supporting 60 frames per second full-load rendering, and a network interface with a throughput of 1000 megabits per second. In the static mapping verification logic, the above parameters are compared with benchmark reference values, and all normalized scores reach a perfect score of 100. Therefore, the extracted physical bottleneck score is 100 points, and the absolute value of the sample variance between each score is zero. After nonlinear exponential penalty calculation (since the variance is zero, the exponential decay term is one), its basic bottleneck computing power index is accurately calibrated to a full score of 100. In the dynamic situation intervention logic, when the multimedia playback reaches its climax, a large amount of reverberation algorithm upgrade process consuming computing resources is artificially injected into the background to simulate a sudden load. At this time, the underlying kernel monitoring socket captures in real time that the CPU load rate rises rapidly to 60%. After weighted moving average filtering and normalization mapping, the processor's availability performance score drops sharply to 40 points, while the other four dynamic indicators remain at 100 points. The system performs multi-dimensional spatial synthesis based on the preset physical allocation coefficients, calculating that the overall dynamic availability performance score under this specific working condition drops to 82 points. In the spatiotemporal synthesis and strategy execution logic, the system applies the first weight coefficient and the second weight coefficient to the full-score basic bottleneck computing power index and the frustrated overall dynamic availability performance score, respectively, and obtains a multi-dimensional physical constraint elasticity situation index of 92.8 points. The subtle contraction of this metric was keenly captured by the reinforcement learning adaptation engine. When faced with an ultra-high-definition test song with extremely high resource consumption and full special effects, the engine abandoned the extreme strategy of directly extracting the highest load, and instead selected a secondary enhancement adaptation strategy through forward inference that hard-specified audio at 96 kHz, video at 2000 Hz resolution and with heavy special effects.

[0106] This execution logic completely solves the problem of memory breakdown and physical crashes caused by blind scheduling when high-end equipment encounters hidden sudden loads. Actual test data shows that after implementing this dimensionality reduction strategy, the hardware frame drop rate reported by the graphics processor remains at an absolute zero, the number of audio buffer data underflow interrupts is zero, and the single-frame hardware decoding time is consistently within 12.1 milliseconds (far below the 16.6 millisecond physical crash threshold). Through extremely precise gliding along the edge of the physical threshold, the objective deterministic physical reward value under this condition remains at a high score of 96.16, perfectly avoiding instantaneous physical exhaustion of system silicon resources without sacrificing the visually perceptible audiovisual continuity.

[0107] In one specific implementation, this embodiment provides verification of specific industrial application test data under a second extreme physical condition. The test scenario targets a low-spec target audio device exhibiting extremely unbalanced architectural characteristics.

[0108] In this embodiment, it should be noted that many inexpensive portable devices on the market, for marketing purposes, often feature processors with high clock speeds but extremely poor storage media and minimal RAM. Traditional algorithms, using simple linear weighted averaging, are easily misled by the high processor score, which masks the storage deficiencies, leading to the allocation of resources beyond the memory throughput limit and causing bus deadlock.

[0109] Specifically, the physical environment was set as a portable Bluetooth speaker. Its actual measured CPU performance score was 50 points, and its GPU score was 50 points. However, its random access memory capacity was extremely small, with a score of only 12.5 points. Its non-volatile media read speed was extremely slow, with a score of only 10 points. Its network bandwidth score was also 10 points.

[0110] In the extreme imbalance penalty logic, the system first uses a minimum value retrieval tool to pinpoint its physical bottleneck score as 10. Then, the system calculates the sample variance among these five performance scores. Due to the coexistence of two scores of 50 and three extremely low scores of around 10, the calculated sample variance is as high as 369. The system substitutes this extremely large variance value into a nonlinear exponential penalty model, exponentially amplifying the physical friction attenuation caused by its imbalance defect. The final calculated basic bottleneck computing power index drops dramatically from a linear average of approximately 26 points and converges to only 1.58 points.

[0111] In the conclusions and technical effect evaluation, this objective computing power reduction logic directly reflects the actual physical throughput capacity of the device in the multimedia pipeline. Upon receiving extreme situation indicators of less than two points, the reinforcement learning adaptation engine strictly limits the exploration boundary of its decision space. The engine directly outputs a basic resource adaptation strategy, forcibly limiting all media tracks to operate at the minimum specifications. This mechanism fundamentally eliminates data congestion caused by the bottleneck (low memory throughput), completely resolving the frequent "memory overflow and screen flickering" and "system freeze" problems when inexpensive unbalanced devices are connected to digital entertainment platforms, thus giving the entire platform an extremely high device compatibility and survivability rate.

[0112] In one specific implementation, this embodiment provides test data verification for a third large-scale concurrent physical condition in a specific industrial application. The test scenario aims to verify the system's global network and storage scheduling performance when a massive number of devices simultaneously initiate resource requests.

[0113] In this embodiment, it should be noted that during peak holiday periods, traditional entertainment platforms are prone to content delivery network (CDN) outbound bandwidth paralysis due to the frequent song switching and full HD resource package downloads by a large number of devices, and the rapid aging and damage of flash memory chips on the end devices due to high-frequency global erasure and writing. Specifically, the physical environment is set up with one thousand target audio devices with completely heterogeneous physical characteristics connected simultaneously under the core cloud node. As a strict control group, another one thousand comparative devices using the traditional "static rule full coverage download" logic are deployed. The test period is 168 hours (seven days) of continuous operation to simulate the dynamic switching of picture quality caused by extremely high frequency of user song switching and device operating condition fluctuations. In the comparison logic of the actual objective physical data, the system relies on bidirectional track differential verification and multi-level streaming media physical composite loading mechanism, transmitting only the spatiotemporal enhancement layer data block and the incremental differential node code that has passed the directed acyclic graph connectivity verification in the network channel. Data statistics show that the overall outbound physical bandwidth peak of the present invention is significantly reduced by 74.3% compared with the control group.

[0114] The more crucial underlying physical metric lies in the device's liveness status at the edge. Under high-intensity stress testing with over 500,000 tracks played cumulatively, the control group experienced an average of 12.8 audio drive buffer underflows (causing audio interruptions) per device per day due to frequent large file downloads and overwriting. In contrast, the device group of this invention, utilizing an extremely lightweight atomic rename submission mechanism and time-slice differential compounding, strictly controls this interruption frequency to an average of 0.02 times per day, a critical level of physical noise.

[0115] Furthermore, regarding the graphics pipeline crash rate, the control group devices, after experiencing frequent additions and removals of special effects, suffered from memory fragmentation, resulting in an inability to allocate large blocks of contiguous memory, leading to a daily frequency of memory overflow crashes and restarts of 1.4 times. In contrast, the device group of this invention, benefiting from a low-level, resident Compare-and-Swap (CAS) spinlock-driven dynamic defragmentation mechanism for physical memory, maintained a zero memory fragmentation crash rate for seven consecutive days, with no device experiencing rendering pipeline deadlocks. This significant data comparison thoroughly demonstrates that this invention is not merely an application-level recommendation algorithm optimization, but a deep, physical-level scheduling optimization that penetrates the operating system kernel, memory bus, and file system. It solves the problem of resource exhaustion that traditional macro-level distribution architectures easily experience under extreme concurrency.

[0116] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for resource adaptation on an entertainment platform, characterized in that, include: Obtain basic hardware parameter information and underlying physical state parameters of the target audio device; Extract the individual performance scores of the basic hardware parameter information, determine the lowest individual performance score as the physical bottleneck score, and calculate the sample variance between the individual performance scores to characterize the unbalance of the hardware architecture. Based on the physical bottleneck score and the sample variance, a nonlinear exponential penalty calculation is performed to obtain the basic bottleneck computing power index. The basic bottleneck computing power index is then combined with the underlying physical state parameters of the hardware through multidimensional spatial transformation and spatiotemporal synthesis to generate a multidimensional physical constraint elastic state index. The multidimensional physical constraint elasticity state index is concatenated with the resource consumption feature vector of the target song to form a joint feature vector, which is then input into a pre-trained reinforcement learning adaptation engine. The reinforcement learning adaptation engine then outputs the target resource adaptation strategy through forward propagation inference. The media track specifications defined by the target resource adaptation strategy are parsed, and bidirectional track differential verification is performed according to the media track specifications currently loaded on the target audio device. Based on the verification results, missing incremental track data blocks are extracted from the multi-level resource pool, and the incremental track data blocks are pushed to the target audio device for timestamp alignment and physical composite loading.

2. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, The acquisition of basic hardware parameter information of the target audio device includes: To detect the underlying kernel architecture of the current operating system; When running in a microkernel operating system environment, it requests an anonymous shared memory region from the memory management unit and maps it to the virtual address space of the hardware monitoring process through the inter-process communication protocol; Based on the memory barrier instructions of the microkernel architecture, and according to the preset double-word alignment rules and the actual physical byte length occupied by each static parameter, the absolute memory read pointer address of each static parameter in the shared memory region is calculated recursively. Based on the absolute memory read pointer address, non-blocking data reading is performed in the virtual address space to obtain the basic hardware parameter information.

3. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, The step of performing multi-dimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters of the hardware to generate a multi-dimensional physical constraint elastic state index includes: A sliding historical window with multiple consecutive sampling periods extending forward is constructed, and a differentiated physical time decay weight coefficient is applied to the real-time collected hardware underlying physical state parameters to perform time series weighted moving average filtering. The filtered situation parameters are uniformly converted into dynamic availability performance sub-scores through normalization mapping, and then physical weighted summation is performed to obtain the overall dynamic availability performance score. Obtain the first weighting coefficient and the second weighting coefficient, use the first weighting coefficient to constrain the basic bottleneck computing power index, use the second weighting coefficient to constrain the dynamic availability performance overall score, and add the two together to generate the multidimensional physical constraint elasticity status index.

4. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, The reinforcement learning adaptation engine uses an objective physical reward function to update network parameters during the offline training phase. The calculation process of the objective physical reward function includes: At the decision cut-off point where media segment playback ends, obtain the graphics processor's hard frame drop rate, the cumulative number of data underflow interrupts in the audio hardware driver buffer, and the media decoder efficiency score obtained based on the segment mapping of average single-frame video hard decoding time. The absolute difference between the multidimensional physical constraint elasticity state index and the mean value of each physical orbit level actually extracted by the current execution strategy is calculated to quantify the degree of mismatch between resources and capabilities, and then the hardware resource utilization efficiency scalar is derived. An objective deterministic physical reward value is generated by comprehensively considering the hard frame drop rate, the cumulative number of data underflow interruptions, the media decoder efficiency score, and the hardware resource utilization efficiency scalar through a preset weight allocation system, and the objective deterministic physical reward value is used for gradient backpropagation.

5. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, The incremental orbit data block contains unstructured visual effects scripts, and the process of physically compounding the unstructured visual effects scripts includes: The target visual effects script and the current local visual effects script are parsed into the target abstract syntax tree and the current abstract syntax tree, respectively. Extract node execution features and generate high-order hash values; extract preliminary incremental difference node sets through set intersection comparison and difference operation. Traverse the input pins of each node in the initial incremental differential node set. When it is determined that there is a parent node that is not included in the initial incremental differential node set and is not included in the local live node hash table, trigger a rendering pipeline semantic break alarm. Reverse physical tracing is performed along the topological path of the target abstract syntax tree, and all missing parent nodes with dependency bearing relationships are forcibly added into the incremental set to form the final incremental differential node set of the physical closed loop. The final incremental differential node set is reorganized into a micro incremental syntax tree and deserialized into a physical byte stream text, which is then dynamically compiled and linked to the shader execution queue of the target audio device at runtime.

6. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, During the process of pushing the incremental track data block to the target audio device for loading, a two-phase commit and hash verification mechanism is used to ensure data transaction integrity, specifically including: During the preparation phase, a temporary physical sector in a logically isolated state is requested from the operating system's file system. All received incremental track data blocks are sequentially written into the temporary physical sector, and cyclic redundancy check codes are calculated in blocks. During the verification and submission phase, the complete bit stream of the temporary physical sector data is read to calculate the overall physical integrity hash value, and the overall physical integrity hash value is compared with the theoretical target integrity hash value carried by the issued task to determine the physical bit-level equivalence. If the determination result is true, the original resource file pointer is deleted through an atomic renaming operation, and the pointer of the temporary physical sector is mounted to the standard resource directory tree to complete the commit; if the determination result is false or the system is in a sudden power outage and restart state, a forced jump to the rollback stage is performed, and a hardware erase operation is performed on all sector bits of the temporary physical sector at the physical addressing level, and the pointer is anchored to the absolutely complete old version baseline resource file.

7. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, During the process of pushing the incremental track data block to the target audio device for loading, dynamic defragmentation of the rendering pipeline memory based on physical time slices is triggered, specifically including: Scan the physical address mapping table of the video random access memory, calculate the ratio of the largest contiguous free physical memory segment to the sum of all free physical memory blocks, and derive the dimensionless video memory fragmentation physical index. When the dimensionless memory fragmentation physical index exceeds the set threshold, calculate the physical time consumed by the pre-rendering task in the current rendering frame, and deduce the remaining safe physical time window of the current frame by combining the hard physical cycle benchmark and the safety redundancy margin. Based on the physical read / write bandwidth of the graphics processor's memory bus, calculate the maximum byte size of the surviving texture block that can be safely moved within the safe physical time window; Select surviving texture resource blocks that meet the maximum byte size limit and physically copy them from the low address space to a contiguous high address space; By comparing and swapping spinlocks at the hardware instruction level, under the unified virtual memory management mechanism, it is detected whether the unified virtual memory pointer strictly points to the old physical base address before the relocation. Only when the detection passes, the unified virtual memory pointer is overwritten and redirected to the new physical base address after the relocation within an indivisible physical clock cycle.

8. The method for resource adaptation of an entertainment platform according to claim 1, characterized in that, The reinforcement learning adaptation engine also integrates priority experience replay control logic during the offline training phase, specifically including: The joint feature vector at the decision moment, the target resource adaptation strategy executed, the objective deterministic physical reward value generated by the settlement, and the next state are encapsulated into a physical decision quadruple state vector and pushed into the experience replay buffer. Calculate the absolute value of the time difference error for each quadruple sample, so that the probability of the quadruple sample being selected is positively correlated with the absolute value of its corresponding time difference error, thereby forcibly increasing the selection frequency of samples at the severe physical working conditions boundary. Within a preset training step size, samples are randomly selected in batches according to the sampling probability and transferred to the optimizer for network parameter updates, and hard synchronization overlay of the target network parameters is performed periodically.

9. A resource adaptation system for an entertainment platform, characterized in that, A method for performing resource adaptation of an entertainment platform according to any one of claims 1 to 8, the system comprising: The device parameter acquisition module is used to acquire basic hardware parameter information and underlying physical status parameters of the target audio device; The performance evaluation module is used to extract the individual performance scores of the basic hardware parameter information, determine the lowest individual performance score as the physical bottleneck score, calculate the sample variance between the individual performance scores, perform nonlinear exponential penalty calculation to obtain the basic bottleneck computing power index, and perform multidimensional spatial transformation and spatiotemporal synthesis of the basic bottleneck computing power index and the underlying physical state parameters of the hardware to generate a multidimensional physical constraint elastic state index. A reinforcement learning adaptation engine is used to combine the multidimensional physical constraint elasticity status index with the resource consumption feature vector of the target song, and output the target resource adaptation strategy through forward propagation inference. The resource scheduling module is used to parse media track specifications and perform bidirectional track differential verification. It extracts missing incremental track data blocks from the multi-level resource pool and pushes them to the target audio device for timestamp alignment and physical composite loading.

10. An electronic device, characterized in that, include: The device includes an internally connected non-volatile memory, a multi-core central processing unit, a random access memory, a graphics processing unit, and a network physical communication module. The non-volatile memory persistently stores a set of machine code instructions. After the device is powered on and reset, the multi-core central processing unit loads the set of machine code instructions into the random access memory for decoding and execution, thereby implementing the resource adaptation method of the entertainment platform according to any one of claims 1 to 8.

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