A chip signal interruption response optimization method and system based on timing perception

Abstract

The application relates to the technical field of response optimization, and discloses a chip signal interruption response optimization method and system based on time sequence sensing, which comprises the following steps: time domain characteristic tracking is performed on a real-time interruption request sequence of a target chip signal processing pipeline to obtain interruption flow time sequence characteristics; a prospective window resource planning is performed on global schedulable resources of the target chip signal processing pipeline to obtain resource pre-allocation window parameters; the global schedulable resources are divided into special logic resource areas according to the resource pre-allocation window parameters, and key parameter analysis is performed on the real-time interruption request sequence to obtain processing demand identifiers; resource allocation is performed on the real-time interruption request sequence, and configuration pre-loading is performed on the sequence after the resource allocation to obtain an instant available environment; instant scheduling triggering is performed on the real-time interruption request sequence to obtain a processing result; and the application can improve the efficiency of chip signal interruption response.

Description

Technical Field

[0001] This invention relates to the field of response optimization technology, and in particular to a time-aware chip signal interruption response optimization method and system. Background Technology

[0002] Existing chip signal interrupt response technologies generally rely on CPU software interrupt handling mechanisms, using operating system scheduling to achieve response and resource management. This mode requires repeated context saving and restoration when handling interrupts, consuming a large number of CPU computing cycles and memory bandwidth. Moreover, the response latency is significantly affected by system scheduling, and under heavy load, it is prone to uncontrollable delays, which may cause problems such as display abnormalities, device stagnation, or firmware timeouts. At the same time, if the interrupt nesting and masking strategies are not designed properly, they will further exacerbate the response latency and interrupt loss risk.

[0003] Traditional interrupt resource allocation employs a static pre-allocation strategy, which remains fixed after system startup and cannot be flexibly adjusted according to real-time changes in the interrupt request flow. When a large number of sudden interrupts flood in, the fixed resource area is prone to resource collisions and channel contention due to a lack of buffer margin. Conversely, during periods of sparse interrupts, reserved resources remain idle, resulting in significant waste. Furthermore, existing solutions lack deep perception of the timing characteristics of interrupt requests, failing to distinguish between steady-state and sudden modes. They also cannot perform forward-looking resource planning and configuration pre-loading before an interrupt arrives, forcing each interrupt to undergo a complete resource search, allocation, and loading process, thus limiting response speed and resource utilization efficiency. Therefore, improving the efficiency of chip signal interrupt response has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a timing-aware chip signal interruption response optimization method and system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides a timing-aware chip signal interruption response optimization method, comprising: Step 1: Perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence; Step 2: Based on the interrupt flow timing characteristics, perform forward-looking window resource planning on the global schedulable resources of the target chip signal processing pipeline to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics; Step 3: Based on the resource pre-allocation window parameters, divide the globally schedulable resources into dedicated logical resource areas, and perform key parameter parsing on the real-time interrupt request sequence to obtain the processing requirement identifier of the real-time interrupt request sequence. Step 4: Based on the dedicated logical resource area and the processing requirement identifier, allocate resources to the real-time interruption request sequence, and preload the configuration of the sequence after resource allocation to obtain the immediate availability environment of the real-time interruption request sequence. Step 5: Based on the immediate availability environment, the real-time interruption request sequence is scheduled and triggered in real time to obtain the processing result of the real-time interruption request sequence.

[0006] In a preferred embodiment, the step of performing time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence includes: The real-time interrupt request sequence of the target chip signal processing pipeline is correlated and segmented to obtain the interrupt grouping of the real-time interrupt request sequence; Based on the interruption grouping, adaptive attribute fusion is performed on the real-time interruption request sequence to obtain the grouping representation of the real-time interruption request sequence; The arrival interval pattern of the group representation is identified to obtain the key interval pattern of the group representation. Based on the key interval pattern, the grouping representation is statistically encapsulated to obtain the interrupt flow timing characteristics of the real-time interruption request sequence.

[0007] In a preferred embodiment, the step of performing forward-looking window resource planning on the globally schedulable resources of the target chip's signal processing pipeline based on the interrupt flow timing characteristics to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics includes: The timing characteristics of the interrupted flow are separated into steady-state mode components and transient mode components; The steady-state mode components are defined by time-series resource baseline to obtain the baseline resource time slots of the steady-state mode components; Non-uniform density estimation is performed on the transient mode components to obtain the burst probability distribution of the transient mode components; Based on the burst probability distribution, an elastic buffer is injected into the baseline resource time slot to obtain the buffer resource band of the baseline resource time slot; The potential resource collision risks of the buffer resource band are identified, and based on the potential resource collision risks, the buffer resource band is virtually partitioned into intra-band channels to obtain the micro-resource channels of the buffer resource band. The baseline resource time slot and the micro-resource channel are jointly parameterized to obtain the resource pre-allocation window parameters of the interruption flow timing characteristics.

[0008] In a preferred embodiment, the step of performing non-uniform density estimation on the transient mode components to obtain the burst probability distribution of the transient mode components includes: The pulse interval of the transient mode component is derived to obtain the interval sequence of the transient mode component; The interval sequence is divided into sliding window segments to obtain local interval windows of the interval sequence; Extract the temporal anchor points of the local interval windows and determine the center time of each local interval window; The local interval window is evaluated for neighborhood interval dispersion to obtain the local variation coefficient of the local interval window. Based on the local variation coefficient, the burst probability density value of the transient mode component is determined. The burst probability density values ​​are time-domain normalized and integrated to obtain the burst probability distribution of the transient mode components.

[0009] In a preferred embodiment, dividing the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters includes: Spatiotemporal element deconstruction is performed on the resource pre-allocation window parameters to obtain the time anchor point and spatial requirements of the resource pre-allocation window parameters; Based on the time anchor point and the spatial requirements, a topology adaptation mapping is performed on the globally schedulable resources to obtain a resource distribution map of the globally schedulable resources; The resource distribution map is reconfigured based on affinity to obtain the resource association map of the resource distribution map; The resource association graph is clustered to obtain the dedicated logical resource area of ​​the globally schedulable resources.

[0010] In a preferred embodiment, the step of parsing key parameters of the real-time interruption request sequence to obtain the processing requirement identifier of the real-time interruption request sequence includes: Extract the original parameters of the real-time interrupt request sequence and identify the explicit priority of the original parameters to obtain the parameter priority sequence of the real-time interrupt request sequence. Based on the parameter priority sequence, the data dependency relationship of the original parameters is derived to obtain the implicit dependency relationship graph of the original parameters; The semantic intent of the original parameters is obtained by jointly inferring the original parameters and the implicit dependency graph. The implicit dependency graph is uniquely encoded and mapped to the semantic intent to obtain the processing requirement identifier of the real-time interruption request sequence.

[0011] In a preferred embodiment, the step of allocating resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and preloading the configuration of the sequence after resource allocation to obtain an immediately available environment for the real-time interruption request sequence, includes: The logical carrying capacity of the dedicated logical resource area is evaluated to obtain the resource efficiency of the dedicated logical resource area; The processing requirement identifier is matched with the resource performance in a multi-dimensional adaptive manner, and the real-time interruption request sequence is mapped to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence. Based on the static logical relationship, the dedicated logical resource area is logically isolated and copied to obtain the shadow space of the dedicated logical resource area; The configuration parameters of the static logical relationship are obtained by performing configuration derivation on the static logical relationship. Based on the complete configuration parameters, the shadow space is asynchronously preloaded and filled to obtain an instantly available environment for the real-time interruption request sequence.

[0012] In a preferred embodiment, the step of performing multi-dimensional adaptive matching between the processing demand identifier and the resource performance, and mapping the real-time interruption request sequence to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence includes: The requirement dimension of the processing requirement identifier is parsed to obtain the requirement feature scalar of the processing requirement identifier. Based on the demand feature scalar, the resource efficiency is dimensionally decomposed to obtain the efficiency feature scalar of the resource efficiency; Based on the demand characteristic scalar and the performance characteristic scalar, determine the matching decision value between the real-time interruption request sequence and the dedicated logical resource area; Based on the matching decision value, a global optimal mapping search is performed on the real-time interruption request sequence and the dedicated logical resource area to obtain the correspondence between the real-time interruption request sequence and the dedicated logical resource area; The topological arrangement of the correspondence is performed to obtain the static logical relationship of the real-time interruption request sequence.

[0013] In a preferred embodiment, the step of instantly scheduling and triggering the real-time interruption request sequence based on the immediately available environment to obtain the processing result of the real-time interruption request sequence includes: When the immediate available environment is in a ready state, the real-time interruption request sequence is triggered and granted, and the triggering license credential of the real-time interruption request sequence is obtained. Based on the triggering license credential, the context-encoded ready-to-use environment is used to obtain the activation instruction for the ready-to-use environment; The activation command is dispatched in a time sequence, and the execution process of the activation command is monitored to obtain the processing result of the real-time interruption request sequence.

[0014] To address the aforementioned problems, the present invention also provides a timing-aware chip signal interruption response optimization system, the system comprising: The timing feature extraction module is used to perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing features of the real-time interrupt request sequence. The resource forward planning module is used to perform forward window resource planning on the globally schedulable resources of the target chip signal processing pipeline based on the interrupt flow timing characteristics, and obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics. The resource partitioning and demand parsing module is used to partition the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters, and to parse the key parameters of the real-time interruption request sequence to obtain the processing demand identifier of the real-time interruption request sequence. The resource adaptation and environment preloading module is used to allocate resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and to preload the configuration of the sequence after resource allocation to obtain the immediate available environment of the real-time interruption request sequence. The real-time scheduling and execution module is used to perform real-time scheduling and triggering of the real-time interruption request sequence based on the real-time availability environment, and obtain the processing result of the real-time interruption request sequence.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses a timing-aware mechanism to track and recognize interrupt request flows in real time, and pre-loads resource allocation and configuration based on forward-looking window resource planning. This allows interrupts to be processed directly in an immediately available environment, eliminating the serial delay of resource lookup, allocation, and configuration loading after an interrupt arrives, as in traditional solutions. Simultaneously, this method avoids frequent CPU context saving and restoration operations, resulting in deterministic low-latency interrupt responses and significantly improving the response speed and system stability of the chip's signal processing pipeline in high-frequency interrupt scenarios.

[0016] 2. This invention employs a dynamic resource planning and dedicated logic resource area partitioning strategy, enabling flexible adjustment of resource allocation based on the steady-state and transient burst modes of the interrupt flow. This provides an elastic buffer channel to prevent resource collisions during burst interrupts and releases idle resources when interrupts are sparse, significantly improving the utilization efficiency of globally schedulable resources. Furthermore, by parsing key parameters of interrupt requests and generating processing requirement identifiers, combined with multi-dimensional adaptive matching, precise correspondence between resources and requirements is achieved. This gives the system a deep understanding of temporal changes in the interrupt flow and adaptive scheduling capabilities, effectively overcoming the resource waste and response bottleneck problems caused by static pre-allocation strategies. Attached Figure Description

[0017] Figure 1 A flowchart illustrating a timing-aware chip signal interruption response optimization method according to an embodiment of the present invention; Figure 2 A functional block diagram of a timing-aware chip signal interrupt response optimization system provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides a timing-aware chip signal interruption response optimization method. The execution entity of this timing-aware chip signal interruption response optimization method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the timing-aware chip signal interruption response optimization method can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0020] Reference Figure 1 The diagram shown is a flowchart illustrating a timing-aware chip signal interrupt response optimization method according to an embodiment of the present invention. In this embodiment, the timing-aware chip signal interrupt response optimization method includes: Step 1: Perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence; In this embodiment of the invention, the step of performing time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence includes: The real-time interrupt request sequence of the target chip signal processing pipeline is correlated and segmented to obtain the interrupt grouping of the real-time interrupt request sequence; Based on the interruption grouping, adaptive attribute fusion is performed on the real-time interruption request sequence to obtain the grouping representation of the real-time interruption request sequence; The arrival interval pattern of the group representation is identified to obtain the key interval pattern of the group representation. Based on the key interval pattern, the grouping representation is statistically encapsulated to obtain the interrupt flow timing characteristics of the real-time interruption request sequence.

[0021] Analyze the attribute information of each interrupt request in the real-time interrupt request sequence, including the trigger source, processing type, and association identifier of the request. Based on the same or similar association identifiers in these attribute information, interrupt requests with related relationships are classified into the same group to ensure that interrupt requests in the same group are directly related in terms of processing logic or triggering scenario, and finally form multiple independent interrupt groups.

[0022] For each interruption group, the core attributes of all interruption requests within that group are extracted. These attributes cover key information such as request response priority, processing time, resource requirement type, etc. Based on the importance and relevance of the attributes, the attributes of multiple interruption requests within the same group are integrated and refined, and duplicate and redundant attribute information is eliminated. The core attribute set that can reflect the overall characteristics of the group is retained, forming the group representation corresponding to each interruption group.

[0023] Record the arrival time of the interrupt request corresponding to each group representation in the time dimension, calculate the time interval between two adjacent interrupt requests, organize and analyze all the calculated time intervals, find the interval types that recur frequently and have relatively fixed time interval values. These interval types can reflect the regularity of the arrival of interrupt requests corresponding to the group representation, and determine these regular interval types as the key interval patterns of group representation.

[0024] By combining the identified key interval patterns, the arrival time distribution of interrupt requests corresponding to the group representations is summarized, clarifying the frequency and duration range of key interval patterns and their distribution position in the overall time series. At the same time, the core attribute information of the group representations is integrated, and the statistical information related to these key interval patterns and the core attribute information are systematically integrated and packaged to form interrupt flow time series characteristics that can comprehensively reflect the time dimension characteristics of real-time interrupt request sequences.

[0025] The beneficial effects are as follows: by analyzing the attribute information of real-time interruption requests and classifying them into independent interruption groups according to the association identifier, interruption requests related to processing logic or triggering scenarios can be accurately aggregated. Then, the core attributes of each group are extracted and integrated to form a group representation, clearly presenting the overall characteristics of each group of interruption requests. Subsequently, by recording the arrival time and analyzing the time interval, key interval patterns are determined, and the regularity of interruption request arrival is clarified. Finally, the statistical information related to key interval patterns and the core attributes of the groups are integrated to form an interruption flow time sequence feature that can comprehensively reflect the time dimension characteristics of the real-time interruption request sequence, providing a precise basis for subsequent resource planning and scheduling.

[0026] Step 2: Based on the interrupt flow timing characteristics, perform forward-looking window resource planning on the global schedulable resources of the target chip signal processing pipeline to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics; In this embodiment of the invention, the step of performing forward-looking window resource planning on the globally schedulable resources of the target chip signal processing pipeline based on the interrupt flow timing characteristics to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics includes: The timing characteristics of the interrupted flow are separated into steady-state mode components and transient mode components; The steady-state mode components are defined by time-series resource baseline to obtain the baseline resource time slots of the steady-state mode components; Non-uniform density estimation is performed on the transient mode components to obtain the burst probability distribution of the transient mode components; Based on the burst probability distribution, an elastic buffer is injected into the baseline resource time slot to obtain the buffer resource band of the baseline resource time slot; The potential resource collision risks of the buffer resource band are identified, and based on the potential resource collision risks, the buffer resource band is virtually partitioned into intra-band channels to obtain the micro-resource channels of the buffer resource band. The baseline resource time slot and the micro-resource channel are jointly parameterized to obtain the resource pre-allocation window parameters of the interruption flow timing characteristics.

[0027] The step of performing non-uniform density estimation on the transient mode components to obtain the burst probability distribution of the transient mode components includes: The pulse interval of the transient mode component is derived to obtain the interval sequence of the transient mode component; The interval sequence is divided into sliding window segments to obtain local interval windows of the interval sequence; Extract the temporal anchor points of the local interval windows and determine the center time of each local interval window; The local interval window is evaluated for neighborhood interval dispersion to obtain the local variation coefficient of the local interval window. Based on the local variation coefficient, the burst probability density value of the transient mode component is determined. The burst probability density values ​​are time-domain normalized and integrated to obtain the burst probability distribution of the transient mode components.

[0028] By analyzing the time-varying patterns of the interrupted flow timing characteristics, we can distinguish the characteristic components that exhibit a fixed period, stable frequency, and continuous occurrence. These components are identified as steady-state mode components. Meanwhile, we extract the characteristic components that deviate from the fixed pattern, have variable occurrence times, and exhibit no periodicity, and these are identified as transient mode components.

[0029] The frequency and duration of occurrence of steady-state mode components are statistically analyzed within a continuous time period. The basic resource occupation time required to process the component in each unit of time is determined. Based on these basic resource occupation times, continuous and fixed-duration time segments are divided. Each time segment corresponds to the basic resource allocation for processing steady-state mode components. These time segments are called baseline resource time slots.

[0030] Record the specific time of occurrence of each pulse signal in the transient mode component, calculate the time difference between the occurrence times of two adjacent pulse signals in turn, and arrange these continuous time differences in the order of pulse occurrence to form an interval sequence.

[0031] Set a fixed-length time window. Starting from the beginning of the interval sequence, move the window along the sequence with a fixed step size. After each move, the part of the interval sequence contained in the window is a local interval window. Continue until the window covers the entire interval sequence, resulting in multiple continuous and partially overlapping local interval windows.

[0032] Determine the time range corresponding to the interval sequence segment contained in each local interval window, calculate the midpoint between the start and end times of the time range, and the time corresponding to this midpoint is the center time of the local interval window.

[0033] Several adjacent local interval windows are selected as neighborhood windows for each local interval window. The interval values ​​in the local interval window are compared with the interval values ​​in all neighborhood windows. The degree of difference between the two is calculated. Based on the quantification of the degree of difference, an index that can reflect the dispersion of the interval values ​​in the local interval window is formed. This index is the local variation coefficient.

[0034] By combining the discreteness reflected by the local coefficient of variation and referring to the known correspondence of sudden features in similar scenarios, we can determine the probability of a sudden event occurring at the center time corresponding to the local interval window. This probability is then converted into a specific numerical value, which is the sudden event probability density value.

[0035] The formula for calculating the burst probability density value is: ; In the formula, Center moment The burst probability density value at the location, The preset normalization factor, It is the hyperbolic tangent function. Center moment The local coefficient of variation, The statistical mean of the local coefficients of variation. The statistical standard deviation of the local coefficient of variation is given. It is an exponential function. The preset center parameters, This is the preset width parameter.

[0036] The normalization factor is a pre-set fixed value used to uniformly adjust the range of calculation results. The local coefficient of variation (LCV) comes from the evaluation of the dispersion of neighborhood intervals within local interval windows. During the evaluation, several adjacent local interval windows are selected as neighborhood windows for each local interval window. The interval values ​​within the local interval window are compared with those within all neighborhood windows, and the degree of difference is calculated and converted into this coefficient. The statistical mean is obtained by collecting the LCVs of all local interval windows, summing these values, and dividing by the total number of LCVs. The statistical standard deviation is obtained by first calculating the difference between each LCV and the statistical mean, squaring each difference, summing them, dividing by the total number of LCVs, and then taking the square root of the result. The center parameter and width parameter are pre-set fixed values, used to anchor the reference benchmark for the LCVs and control the variation range of the exponential function part, respectively.

[0037] The hyperbolic tangent function takes the correlation between the local coefficient of variation and the statistical mean and standard deviation as input, and maps this result to a fixed range through a specific curve transformation. The transformed result is added to 1 and then multiplied by a normalization factor to standardize the numerical range. The exponential function takes the deviation between the local coefficient of variation and the central parameter, and adjusts the degree of deviation through its own decay characteristics; the greater the deviation, the closer the adjusted result is to the fixed low value. The two processed results are multiplied to obtain the burst probability density value at the central time, which directly quantifies the probability of an interruption burst occurring at the central time.

[0038] When the local coefficient of variation is close to the statistical mean, the result after transformation by the hyperbolic tangent function approaches a fixed value. If the local coefficient of variation is also close to the central parameter at this time, the result after adjustment by the exponential function approaches the maximum value. The combined effect of these two factors leads to a higher burst probability density value. When the local coefficient of variation deviates from the statistical mean, the result after transformation by the hyperbolic tangent function shifts towards both ends of a fixed range. Simultaneously, when the local coefficient of variation deviates from the central parameter, the result after adjustment by the exponential function gradually decreases. The combined effect of these two factors leads to a decrease in the burst probability density value.

[0039] Collect the center time and corresponding burst probability density value corresponding to all local interval windows, arrange these values ​​in chronological order of the center time, adjust the range of all burst probability density values ​​so that they are within a uniform numerical range, and form a sequence that can completely reflect the distribution of burst probability at different times. This sequence is the burst probability distribution.

[0040] Based on the probability distribution of sudden events, the probability of sudden events occurring in different time periods is determined. An additional resource reservation time of a certain duration is added to the baseline resource time slot for time periods with a higher probability of sudden events. These additional resource reservation times are connected with the original baseline resource time slots to form a continuous resource allocation interval, which is the buffer resource zone.

[0041] Analyze the time overlap of different resource allocation intervals within the buffer resource zone, check whether there are multiple resource demands competing for the same resource in the same time period, identify all possible time nodes and resource allocation intervals involved in resource contention, and these identified situations are potential resource collision risks.

[0042] Based on the distribution of potential resource collision risks, the time period with collision risks within the buffer resource zone is divided into multiple independent virtual resource channels. Each virtual resource channel corresponds to an independent resource allocation path, and the resource allocation between different channels does not interfere with each other. These independent virtual resource channels are called micro-resource channels.

[0043] Extract core information such as the duration, start and end times of the baseline resource time slot, as well as key information such as the number of micro-resource channels and the resource allocation range of each channel. Organize and transform this information according to unified rules to form a standardized information set that can completely describe the resource pre-allocation scheme. This set is the resource pre-allocation window parameter.

[0044] The beneficial effects include the ability to accurately separate the steady-state and transient components of the interruption flow's temporal characteristics, establish baseline resource time slots based on the frequency and duration of steady-state components, collect pulse moments, calculate interval windows and local variation coefficients for transient components, and calculate the burst probability density and distribution. A buffer resource band is added to high-burst intervals during the baseline period, resource collision risks are predicted, and independent micro-resource channels are defined. Information is integrated to generate standardized resource pre-allocation window parameters, adapting to steady-state and transient interruption needs and avoiding resource conflicts. Simultaneously, the source of all computational elements is clarified, the burst intensity at the center moment is accurately quantified, and transient burst characteristics are stably and accurately determined, laying a solid data foundation for probability distribution construction and early-stage forward-looking resource planning.

[0045] Step 3: Based on the resource pre-allocation window parameters, divide the globally schedulable resources into dedicated logical resource areas, and perform key parameter parsing on the real-time interrupt request sequence to obtain the processing requirement identifier of the real-time interrupt request sequence. In this embodiment of the invention, dividing the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters includes: Spatiotemporal element deconstruction is performed on the resource pre-allocation window parameters to obtain the time anchor point and spatial requirements of the resource pre-allocation window parameters; Based on the time anchor point and the spatial requirements, a topology adaptation mapping is performed on the globally schedulable resources to obtain a resource distribution map of the globally schedulable resources; The resource distribution map is reconfigured based on affinity to obtain the resource association map of the resource distribution map; The resource association graph is clustered to obtain the dedicated logical resource area of ​​the globally schedulable resources.

[0046] The process of parsing key parameters of the real-time interruption request sequence to obtain the processing requirement identifier of the real-time interruption request sequence includes: Extract the original parameters of the real-time interrupt request sequence and identify the explicit priority of the original parameters to obtain the parameter priority sequence of the real-time interrupt request sequence. Based on the parameter priority sequence, the data dependency relationship of the original parameters is derived to obtain the implicit dependency relationship graph of the original parameters; The semantic intent of the original parameters is obtained by jointly inferring the original parameters and the implicit dependency graph. The implicit dependency graph is uniquely encoded and mapped to the semantic intent to obtain the processing requirement identifier of the real-time interruption request sequence.

[0047] The core time-related information in the resource pre-allocation window parameters is broken down, including key time nodes such as the start time, end time, and duration of resource allocation. At the same time, spatially related content such as resource occupancy range, adaptation logic area, and resource matching requirements are extracted from the parameters. These time nodes are identified as time anchors, and the spatially related content is organized into spatial requirements.

[0048] The system identifies all available entries of globally schedulable resources, clarifies the functional attributes, available time periods, and logical coverage of each resource, determines the availability status of each resource at the corresponding time node based on time anchors, and filters out resources that meet the logical coverage adaptation requirements based on spatial needs. The system then presents the correspondence between the availability status, functional attributes, time anchors, and spatial needs of resources in a visual form, forming a resource distribution map of globally schedulable resources.

[0049] Analyze the functional similarity, available time overlap, and logical scope fit of each resource in the resource distribution map to determine the degree of synergy and adaptation between resources. Adjust resources with similar functions, highly overlapping available time periods, and mutually matching logical scopes to a strongly associated state, and keep resources with different functions, no overlap in available time periods, or mismatched logical scopes in a weakly associated or unassociated state. Based on these relationships, reorganize the resource distribution structure to form a resource association map.

[0050] Based on the correlation strength of resources in the resource association graph, the strongly correlated resource set is divided into independent logical blocks. The resources in each logical block form an efficient and adaptable whole in terms of function, time, and space. Each block corresponds to a specific type of interrupt request processing requirement, ensuring that the resources in the block can work together to meet the corresponding requirements. These divided independent logical blocks are the dedicated logical resource areas for globally schedulable resources.

[0051] Extract all basic information contained in the real-time interruption request sequence. This information covers core aspects related to interruption handling, such as request type, processing time limit, resource usage type, and data format requirements. Use this extracted basic information as the original parameters.

[0052] Based on the preset priority judgment criteria, which clearly stipulates the urgency of processing time limits, the level of business importance corresponding to the request, and other judgment criteria, each raw parameter is classified into levels, and all raw parameters are arranged in order from high to low according to the classified levels to form a parameter priority sequence.

[0053] According to the order of parameter priority sequence, analyze one by one whether the processing of each original parameter requires the processing result of other original parameters as a prerequisite. If the processing of a certain original parameter must depend on the output data of another original parameter, then it is determined that there is a data dependency relationship between the two. With each original parameter as an independent node, connect the nodes with the dependency relationship with a directional line. The direction of the line is from the dependent parameter to the dependent parameter, thereby constructing the implicit dependency relationship graph of the original parameters.

[0054] By deeply analyzing the attributes of each original parameter, including its corresponding processing operation type and data transmission direction, and combining the relationship between the parameter and other parameters in the implicit dependency graph, we can determine the role of the parameter in the entire interrupt handling process. By comprehensively analyzing the role positioning and interrelationship of all original parameters, we can clarify the common interrupt handling goal that these original parameters point to. This goal is the semantic intent of the original parameter.

[0055] A unified coding rule is established to transform the structural features of the implicit dependency graph. A unique coding segment is assigned to different structural features. At the same time, a unique coding segment is assigned to each semantic intent according to the type of semantic intent. The coding segment corresponding to the implicit dependency graph and the coding segment corresponding to the semantic intent are combined and spliced ​​to form a unique identifier, which is the processing requirement identifier of the real-time interruption request sequence.

[0056] The beneficial effects include accurately decomposing the spatiotemporal information of the resource pre-allocation window, establishing time anchors and spatial requirements, sorting out global resource attributes, time periods and logical scopes, selecting suitable resources to generate resource distribution maps, determining the correlation strength based on the fit of resource time periods, functions and logical scopes and constructing resource correlation maps, dividing independent logical blocks according to correlation strength, achieving efficient spatiotemporal adaptation of resources, completing the planning and division of global resources, and completely extracting the original basic parameters of the interruption sequence, classifying parameter priorities according to rules, analyzing parameter dependencies to build dependency relationship maps, locking the semantic intent of parameters, and generating unique processing requirement identifiers based on unified coding rules, providing a unique judgment basis for the accurate matching of interruption requests and resources, effectively ensuring the efficiency of resource collaboration and the orderly and accurate execution of interruption processing procedures.

[0057] Step 4: Based on the dedicated logical resource area and the processing requirement identifier, allocate resources to the real-time interruption request sequence, and preload the configuration of the sequence after resource allocation to obtain the immediate availability environment of the real-time interruption request sequence. In this embodiment of the invention, the step of allocating resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and preloading the configuration of the sequence after resource allocation to obtain an immediately available environment for the real-time interruption request sequence, includes: The logical carrying capacity of the dedicated logical resource area is evaluated to obtain the resource efficiency of the dedicated logical resource area; The processing requirement identifier is matched with the resource performance in a multi-dimensional adaptive manner, and the real-time interruption request sequence is mapped to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence. Based on the static logical relationship, the dedicated logical resource area is logically isolated and copied to obtain the shadow space of the dedicated logical resource area; The configuration parameters of the static logical relationship are obtained by performing configuration derivation on the static logical relationship. Based on the complete configuration parameters, the shadow space is asynchronously preloaded and filled to obtain an instantly available environment for the real-time interruption request sequence.

[0058] The step of performing multi-dimensional adaptive matching between the processing requirement identifier and the resource performance, and mapping the real-time interruption request sequence to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence includes: The requirement dimension of the processing requirement identifier is parsed to obtain the requirement feature scalar of the processing requirement identifier. Based on the demand feature scalar, the resource efficiency is dimensionally decomposed to obtain the efficiency feature scalar of the resource efficiency; Based on the demand characteristic scalar and the performance characteristic scalar, determine the matching decision value between the real-time interruption request sequence and the dedicated logical resource area; Based on the matching decision value, a global optimal mapping search is performed on the real-time interruption request sequence and the dedicated logical resource area to obtain the correspondence between the real-time interruption request sequence and the dedicated logical resource area; The topological arrangement of the correspondence is performed to obtain the static logical relationship of the real-time interruption request sequence.

[0059] The system analyzes key indicators such as the functional limit, continuous runtime, and maximum number of requests that can be processed simultaneously for each logical block in the dedicated logical resource area. Combined with the collaborative capabilities between logical blocks, it comprehensively judges the amount and intensity of interrupt handling tasks that each logical block and the entire dedicated logical resource area can bear, and integrates these comprehensive judgment results to form resource efficiency.

[0060] The process involves breaking down the various requirement dimensions contained in the requirement identifier, including key requirements such as the timeliness requirements for interruption handling, the type of function implementation, the scale of resource consumption, and the data interaction format. Each requirement dimension is described in detail, and these detailed requirements are transformed into specific feature information that can be directly used for matching. This feature information is the requirement feature scalar.

[0061] Referring to the dimensional division method of demand feature scalars, resource efficiency is split into corresponding dimensions. Specific information that can reflect the resource capabilities of each dimension is extracted. For example, the fastest processing speed of logical blocks is extracted in the timeliness dimension, and the maximum amount of resources that can be provided is extracted in the resource occupancy dimension. These specific information after splitting together constitute the efficiency feature scalar.

[0062] Each corresponding dimension of the demand feature scalar and the performance feature scalar is compared one by one to determine the degree of fit between demand and performance on each dimension. The degree of fit of all dimensions is integrated into a unified quantitative result according to fixed rules. This quantitative result can intuitively reflect the degree of matching between the real-time interruption request sequence and the dedicated logical resource area. This result is the matching decision value.

[0063] The formula for calculating the matching decision value is: ; In the formula, The first in the real-time interrupt request sequence The interrupt request and the first interrupt request in the dedicated logic resource area Matching decision values ​​for each logical block. It is an exponential function. The total number of dimensions of the required feature scalar. The first in the real-time interrupt request sequence The first interrupt request A demand characteristic scalar, For the dedicated logical resource area, the first The first logical block A performance characteristic scalar, To find the maximum value function, To find the minimum value function, It is the hyperbolic tangent function. For absolute value operations, For the preset first Adaptive weighting factors for each demand dimension; The demand feature scalar is obtained by decomposing the key requirements of the demand identifier and transforming them into specific feature information. The efficiency feature scalar is obtained by splitting resource efficiency according to the demand feature scalar dimension and extracting resource capability information of each dimension. The adaptive weight factor is a fixed value that is preset according to the importance of different demand dimensions in interruption handling.

[0064] By calculating the difference between the demand and performance characteristic scalars of each dimension, and then subtracting it from 1 after standardization, summation of squares, and exponential transformation, and by simultaneously calculating the weighted sum of the absolute values ​​of the differences of each dimension and then performing hyperbolic tangent transformation, the two results are multiplied to obtain the matching decision value, which quantifies the degree of matching between a single interruption request and a single logical block.

[0065] When the values ​​of each dimension of the demand and efficiency feature scalars match, the transformation results of both parts tend to be close to a fixed high value, and the matching decision value is at a high level; when the dimensional values ​​of the demand and efficiency feature scalars deviate, the transformation results of both parts change accordingly, and the matching decision value is at a low level.

[0066] Iterate through all real-time interrupt requests and logical blocks in the dedicated logical resource area, record the matching decision value of each interrupt request and each logical block, filter out the logical blocks corresponding to the matching decision value for each interrupt request, and ensure that each logical block corresponds to only one interrupt request to avoid resource occupation conflicts, and finally determine the one-to-one correspondence between all interrupt requests and logical blocks.

[0067] Based on the processing order of real-time interrupt requests and combined with the resource call logic of the logical blocks, the established correspondences are sorted and arranged to clarify the order in which each interrupt request is initiated and processed in the corresponding logical block, the hierarchical relationship of resource calls, and the path order of data transmission, thus forming a fixed and unchanging static logical relationship.

[0068] Based on static logical relationships, all logical blocks corresponding to the relationship in the dedicated logical resource area are copied. During the copying process, the functional attributes and configuration rules of the original logical blocks remain unchanged. At the same time, an independent resource call channel is established to completely isolate the copied set of logical blocks from the original dedicated logical resource area. This independent copied set is called the shadow space.

[0069] By deeply analyzing the processing paths, resource call order, and data interaction requirements in the static logical relationships, the configuration information required for each processing step is derived, including the triggering conditions for resource activation, data format conversion rules, and connection parameters between different steps. The configuration information of all processing steps is systematically organized to form a complete configuration parameter covering the entire process.

[0070] Under the premise that the shadow space operates independently and does not affect other operations, various configuration information is loaded into the corresponding logical blocks of the shadow space step by step according to the requirements of complete configuration parameters. This ensures that each logical block is initialized according to the configuration and that all processing links have the conditions to start, ultimately enabling the shadow space to form an immediately available environment that can directly respond to interruption requests.

[0071] The beneficial effects are as follows: First, the indicators and coordination capabilities of the dedicated logical resource area are evaluated to obtain resource efficiency. Then, the demand identifier and resource efficiency are parsed and processed to obtain demand feature scalars and efficiency feature scalars, respectively. Combining the feature scalars with clear sources and reliable values ​​and adaptive weight factors, a matching decision value is obtained through standardized calculation. The matching degree between interruption requests and logical blocks is accurately quantified. Based on this value, a unique static logical relationship between the two is established. The corresponding logical blocks are replicated to build an independent shadow space. The entire set of configuration parameters is calculated to complete asynchronous preloading and quickly generate an immediately usable environment. This not only relies on the quantitative matching results to achieve accurate and conflict-free resource allocation, but also provides a stable basis for resource pairing, effectively improving the efficiency of interrupt preprocessing and real-time response speed.

[0072] Step 5: Based on the immediate availability environment, the real-time interruption request sequence is scheduled and triggered in real time to obtain the processing result of the real-time interruption request sequence.

[0073] In this embodiment of the invention, the step of instantly scheduling and triggering the real-time interruption request sequence based on the immediately available environment to obtain the processing result of the real-time interruption request sequence includes: When the immediate available environment is in a ready state, the real-time interruption request sequence is triggered and granted, and the triggering license credential of the real-time interruption request sequence is obtained. Based on the triggering license credential, the context-encoded ready-to-use environment is used to obtain the activation instruction for the ready-to-use environment; The activation command is dispatched in a time sequence, and the execution process of the activation command is monitored to obtain the processing result of the real-time interruption request sequence.

[0074] Check the configuration loading status of all logical blocks in the ready-to-use environment, confirm whether each logical block has been initialized with complete configuration parameters, and verify whether the data transmission channel between logical blocks is smooth and whether resource call permissions are enabled. Compare each block with the preset readiness criteria. If the criteria are met, the verification is considered passed; otherwise, the verification is considered failed. The result of the verification is used as the basis to issue a certificate that allows the real-time interruption request sequence to start processing. This certificate is the trigger permission certificate.

[0075] Extract the permission information and the identification information of the real-time interruption request sequence from the trigger license certificate. At the same time, collect the current status information of the immediately available environment, including the running preparation status of logical blocks, resource usage, data interaction paths, etc. Convert this information into a character sequence in a unified format according to preset encoding rules. Each character sequence corresponds to an environment configuration or permission description. Combine all the character sequences in a fixed order to form an instruction that can activate the immediately available environment. This instruction is the activation instruction.

[0076] According to the processing order of the real-time interruption request sequence, the activation instructions are sent sequentially to the corresponding logical blocks in the immediately available environment. During the sending process, the dispatch time and target logical block of each instruction are recorded. During the execution of the instructions, the running status of each logical block is tracked in real time, and the instruction start time, processing progress, and data input and output status are recorded. If an execution exception occurs, the exception type and occurrence time are recorded. After all activation instructions have been executed, the processing results corresponding to each real-time interruption request are collected and organized to form the processing results of the real-time interruption request sequence.

[0077] The beneficial effects are as follows: by comprehensively verifying the configuration loading status of all logical blocks in the immediately available environment, the smoothness of data transmission channels, and the opening status of resource call permissions, the verification results are accurately determined according to preset readiness standards. Compliant triggering licenses are issued to the real-time interruption request sequence to ensure the rigor of triggering authorization. Then, the key information of the triggering license and the real-time interruption request sequence is extracted, and the operation-related status information of the immediately available environment is collected. The information is transformed and combined according to preset encoding rules to form activation instructions, ensuring that the instructions can accurately activate the corresponding environment. Finally, the activation instructions are dispatched in the order of processing and the operation status of logical blocks is tracked in real time. Various execution-related information is recorded. After all instructions have been executed, the processing results of all real-time interruption requests are collected and sorted out to ensure the orderliness of interruption request scheduling and triggering and the completeness of processing results, thereby improving the reliability and efficiency of real-time interruption request processing.

[0078] like Figure 2 The diagram shown is a functional block diagram of a timing-aware chip signal interruption response optimization system provided in an embodiment of the present invention.

[0079] The timing-aware chip signal interrupt response optimization system 100 described in this invention can be installed in an electronic device. Depending on the functions implemented, the timing-aware chip signal interrupt response optimization system 100 may include a timing feature extraction module 101, a resource prospective planning module 102, a resource partitioning and demand analysis module 103, a resource adaptation and environment preloading module 104, and a real-time scheduling and execution module 105. The modules described in this invention can also be referred to as units, which are a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, stored in the memory of the electronic device.

[0080] In this embodiment, the functions of each module / unit are as follows: The timing feature extraction module 101 is used to perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing features of the real-time interrupt request sequence. The resource forward planning module 102 is used to perform forward window resource planning on the globally schedulable resources of the target chip signal processing pipeline based on the interrupt flow timing characteristics, so as to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics. The resource partitioning and demand parsing module 103 is used to partition the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters, and to parse the key parameters of the real-time interruption request sequence to obtain the processing demand identifier of the real-time interruption request sequence. The resource adaptation and environment preloading module 104 is used to allocate resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and to preload the configuration of the sequence after resource allocation to obtain the immediate available environment of the real-time interruption request sequence. The real-time scheduling execution module 105 is used to perform real-time scheduling and triggering on the real-time interruption request sequence based on the real-time availability environment, and obtain the processing result of the real-time interruption request sequence.

[0081] In the several embodiments provided by this invention, it should be understood that the proposed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0082] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0084] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0085] The embodiments of this application can acquire and process relevant data based on an artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A time-aware chip signal interrupt response optimization method, characterized in that, The method includes: Step 1: Perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence; Step 2: Based on the interrupt flow timing characteristics, perform forward-looking window resource planning on the global schedulable resources of the target chip signal processing pipeline to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics; Step 3: Based on the resource pre-allocation window parameters, divide the globally schedulable resources into dedicated logical resource areas, and perform key parameter parsing on the real-time interrupt request sequence to obtain the processing requirement identifier of the real-time interrupt request sequence. Step 4: Based on the dedicated logical resource area and the processing requirement identifier, allocate resources to the real-time interruption request sequence, and preload the configuration of the sequence after resource allocation to obtain the immediate availability environment of the real-time interruption request sequence. Step 5: Based on the immediate availability environment, the real-time interruption request sequence is scheduled and triggered in real time to obtain the processing result of the real-time interruption request sequence.

2. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, The step of tracing the time-domain characteristics of the real-time interrupt request sequence of the target chip's signal processing pipeline to obtain the interrupt flow timing characteristics of the real-time interrupt request sequence includes: The real-time interrupt request sequence of the target chip signal processing pipeline is correlated and segmented to obtain the interrupt grouping of the real-time interrupt request sequence; Based on the interruption grouping, adaptive attribute fusion is performed on the real-time interruption request sequence to obtain the grouping representation of the real-time interruption request sequence; The arrival interval pattern of the group representation is identified to obtain the key interval pattern of the group representation. Based on the key interval pattern, the grouping representation is statistically encapsulated to obtain the interrupt flow timing characteristics of the real-time interruption request sequence.

3. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, Based on the interrupt flow timing characteristics, a forward-looking window resource planning process is performed on the globally schedulable resources of the target chip's signal processing pipeline to obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics, including: The timing characteristics of the interrupted flow are separated into steady-state mode components and transient mode components; The steady-state mode components are defined by time-series resource baseline to obtain the baseline resource time slots of the steady-state mode components; Non-uniform density estimation is performed on the transient mode components to obtain the burst probability distribution of the transient mode components; Based on the burst probability distribution, an elastic buffer is injected into the baseline resource time slot to obtain the buffer resource band of the baseline resource time slot; The potential resource collision risks of the buffer resource band are identified, and based on the potential resource collision risks, the buffer resource band is virtually partitioned into intra-band channels to obtain the micro-resource channels of the buffer resource band. The baseline resource time slot and the micro-resource channel are jointly parameterized to obtain the resource pre-allocation window parameters of the interruption flow timing characteristics.

4. The chip signal interruption response optimization method based on timing awareness as described in claim 3, characterized in that, The step of performing non-uniform density estimation on the transient mode components to obtain the burst probability distribution of the transient mode components includes: The pulse interval of the transient mode component is derived to obtain the interval sequence of the transient mode component; The interval sequence is divided into sliding window segments to obtain local interval windows of the interval sequence; Extract the temporal anchor points of the local interval windows and determine the center time of each local interval window; The local interval window is evaluated for neighborhood interval dispersion to obtain the local variation coefficient of the local interval window. Based on the local variation coefficient, the burst probability density value of the transient mode component is determined. The burst probability density values ​​are time-domain normalized and integrated to obtain the burst probability distribution of the transient mode components.

5. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, The step of dividing the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters includes: Spatiotemporal element deconstruction is performed on the resource pre-allocation window parameters to obtain the time anchor point and spatial requirements of the resource pre-allocation window parameters; Based on the time anchor point and the spatial requirements, a topology adaptation mapping is performed on the globally schedulable resources to obtain a resource distribution map of the globally schedulable resources; The resource distribution map is reconfigured based on affinity to obtain the resource association map of the resource distribution map; The resource association graph is clustered to obtain the dedicated logical resource area of ​​the globally schedulable resources.

6. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, The process of parsing key parameters of the real-time interruption request sequence to obtain the processing requirement identifier of the real-time interruption request sequence includes: Extract the original parameters of the real-time interrupt request sequence and identify the explicit priority of the original parameters to obtain the parameter priority sequence of the real-time interrupt request sequence. Based on the parameter priority sequence, the data dependency relationship of the original parameters is derived to obtain the implicit dependency relationship graph of the original parameters; The semantic intent of the original parameters is obtained by jointly inferring the original parameters and the implicit dependency graph. The implicit dependency graph is uniquely encoded and mapped to the semantic intent to obtain the processing requirement identifier of the real-time interruption request sequence.

7. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, The process of allocating resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and pre-loading the configuration of the sequence after resource allocation to obtain an immediately available environment for the real-time interruption request sequence, includes: The logical carrying capacity of the dedicated logical resource area is evaluated to obtain the resource efficiency of the dedicated logical resource area; The processing requirement identifier is matched with the resource performance in a multi-dimensional adaptive manner, and the real-time interruption request sequence is mapped to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence. Based on the static logical relationship, the dedicated logical resource area is logically isolated and copied to obtain the shadow space of the dedicated logical resource area; The configuration parameters of the static logical relationship are obtained by performing configuration derivation on the static logical relationship. Based on the complete configuration parameters, the shadow space is asynchronously preloaded and filled to obtain an instantly available environment for the real-time interruption request sequence.

8. The chip signal interruption response optimization method based on timing awareness as described in claim 7, characterized in that, The step of performing multi-dimensional adaptive matching between the processing requirement identifier and the resource performance, and mapping the real-time interruption request sequence to the dedicated logical resource area to obtain the static logical relationship of the real-time interruption request sequence includes: The requirement dimension of the processing requirement identifier is parsed to obtain the requirement feature scalar of the processing requirement identifier. Based on the demand feature scalar, the resource efficiency is dimensionally decomposed to obtain the efficiency feature scalar of the resource efficiency; Based on the demand characteristic scalar and the performance characteristic scalar, determine the matching decision value between the real-time interruption request sequence and the dedicated logical resource area; Based on the matching decision value, a global optimal mapping search is performed on the real-time interruption request sequence and the dedicated logical resource area to obtain the correspondence between the real-time interruption request sequence and the dedicated logical resource area; The topological arrangement of the correspondence is performed to obtain the static logical relationship of the real-time interruption request sequence.

9. The chip signal interruption response optimization method based on timing awareness as described in claim 1, characterized in that, The step of instantly scheduling and triggering the real-time interruption request sequence based on the immediately available environment to obtain the processing result of the real-time interruption request sequence includes: When the immediate available environment is in a ready state, the real-time interruption request sequence is triggered and granted, and the triggering license credential of the real-time interruption request sequence is obtained. Based on the triggering license credential, the context-encoded ready-to-use environment is used to obtain the activation instruction for the ready-to-use environment; The activation command is dispatched in a time sequence, and the execution process of the activation command is monitored to obtain the processing result of the real-time interruption request sequence.

10. A timing-aware chip signal interrupt response optimization system, characterized in that, The system is used to implement the timing-aware chip signal interruption response optimization method according to claim 1, the system comprising: The timing feature extraction module is used to perform time-domain characteristic tracking on the real-time interrupt request sequence of the target chip signal processing pipeline to obtain the interrupt flow timing features of the real-time interrupt request sequence. The resource forward planning module is used to perform forward window resource planning on the globally schedulable resources of the target chip signal processing pipeline based on the interrupt flow timing characteristics, and obtain the resource pre-allocation window parameters of the interrupt flow timing characteristics. The resource partitioning and demand parsing module is used to partition the globally schedulable resources into dedicated logical resource areas according to the resource pre-allocation window parameters, and to parse the key parameters of the real-time interruption request sequence to obtain the processing demand identifier of the real-time interruption request sequence. The resource adaptation and environment preloading module is used to allocate resources to the real-time interruption request sequence based on the dedicated logical resource area and the processing requirement identifier, and to preload the configuration of the sequence after resource allocation to obtain the immediate available environment of the real-time interruption request sequence. The real-time scheduling and execution module is used to perform real-time scheduling and triggering of the real-time interruption request sequence based on the real-time availability environment, and obtain the processing result of the real-time interruption request sequence.

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