Intelligent identification method for classifying intrusion events in security areas

Abstract

The present application belongs to the field of security monitoring digital signal processing technology, and relates to a kind of hierarchical security area invasion event intelligent identification method, comprising: obtaining the security weight score of the region to be identified;Sampling video signal and generating stream data message composed of binary bit stream;Compare adjacent data frames to extract feature disturbance offset;Real-time monitoring of the instantaneous load rate of data bus;Coupling weight score and feature disturbance offset, introduce instantaneous load rate to calculate the task scheduling priority of stream data message;When the task scheduling priority exceeds the dynamic adaptive threshold, drive the central processor to start the fast processing channel based on level 1 cache prefetching, the present application dynamically adjusts the data access threshold through the load feedback mechanism, effectively eliminates the risk of buffer overflow, and enhances the high-risk data priority analysis capability of the system under extreme overload working conditions.

Description

Technical Field

[0001] This invention belongs to the field of digital signal processing technology for security monitoring, and particularly relates to an intelligent identification method for intrusion events in graded security areas. It is used for resource scheduling and identification scenarios with concurrent input of multiple heterogeneous monitoring pixel tensor streams, which can effectively eliminate the risk of data buffer overflow and enhance the system's ability to prioritize the parsing of high-risk data under extreme overload conditions. Background Technology

[0002] Currently, when processing large-scale heterogeneous monitoring pixel feature tensor streams, electronic digital processing systems typically use deep learning models to classify and identify targets. In systems with multi-level defense attributes, processors usually allocate computing resources to different levels of regions according to preset security weights. However, conventional resource scheduling protocols exhibit static characteristics, ignoring the dynamic evolution of feature entropy within the data stream, leading to logical redundancy in the processor.

[0003] In existing technical solutions, Chinese patent document CN101674614A discloses a method for ensuring the data transmission quality of security monitoring in a wireless local area network. This method monitors the communication bandwidth through the access point of the wireless device and allocates bandwidth according to the priority of each wireless node. The wireless node then automatically adjusts the data bitstream based on the obtained bandwidth. Although this solution optimizes network resources and reduces latency, its adjustment mechanism mainly focuses on bandwidth allocation at the transmission layer. It lacks the underlying processing logic that perceives the entropy change of the video pixel data itself, and its preset priority logic is relatively rigid, making it difficult to cope with complex and ever-changing physical space feature disturbances.

[0004] Another patent document, CN102316311B, discloses a comprehensive video surveillance scheduling system and method. It associates the video surveillance front end with the scheduling object through a linkage structure, enabling the display of all on-site images of the corresponding scheduling object by operating the scheduling subsystem during the scheduling process. This system significantly improves the convenience and timeliness of monitoring and scheduling. However, it is essentially an integrated system at the management and display level and does not solve the problems of instruction stream congestion and high latency in key feature recognition caused by static resource scheduling under extreme conditions of a large influx of multi-channel heterogeneous data.

[0005] Furthermore, Chinese patent document CN120912407A discloses a security risk intelligent response method and system, which integrates device online rate, network bandwidth and overall computing resource status parameters to construct a dynamic weighted model and configures a fixed priority sorting logic to eliminate command congestion; however, this dynamic response mechanism implicitly relies on the idealized assumption of a homogeneous distribution of data importance within the same priority, and its overall dimension parameter weighting cannot penetrate into the underlying pixel tensor, making it difficult to capture the drastic changes in data entropy caused by transient strong burst disturbances, which may increase the bus load due to redundant parameter polling under extreme conditions.

[0006] Therefore, how to construct a processing mechanism that can sense the entropy change of data stream characteristics and realize dynamic arbitration of computing resources, so as to eliminate the risk of buffer overflow and enhance the parsing capability under extreme overload conditions, has become an urgent technical problem to be solved in the field of digital signal processing for security monitoring. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and solve the technical problems of instruction stream congestion and high key feature recognition latency caused by static resource scheduling when multiple heterogeneous data streams flood into the electronic digital processing system, and to provide an intelligent identification method for intrusion events in graded security areas.

[0008] To achieve the above-mentioned objectives, the present invention provides an intelligent identification method for intrusion events in graded security zones, comprising the following steps: Step S101, Obtain the regional security weight: Obtain the security weight score of the corresponding region to be identified; Step S102, Sample and generate binary stream message: Sample the video signal sequence of the area to be identified and generate a streaming data message consisting of a binary bit stream; Step S103, extract feature perturbation offset: compare the adjacent data frames of the streaming data packet and use a logical XOR gate to extract the feature perturbation offset that represents the severity of bit flipping. Step S104, obtain bus load rate: monitor the instantaneous load rate of the data bus inside the electronic digital data processing system in real time, wherein the instantaneous load rate is determined by the proportion of the bus being occupied within a statistical unit time period; Step S105, calculate instruction scheduling priority: couple security weight score and feature disturbance offset, and introduce instantaneous load rate as denominator into weighted calculation logic to determine instruction scheduling priority that characterizes the timeliness requirements of streaming data packet processing; Step S106, drive to enable high-speed cache fast channel: compare instruction scheduling priority with dynamic adaptive threshold. When the instruction scheduling priority exceeds the dynamic adaptive threshold, drive the central processing unit to enable a fast processing channel based on the first-level instruction cache prefetch for streaming data packets, and suspend low-priority task instructions in the instruction queue. Use the central processing unit's instruction dispatch engine to rearrange the instruction pipeline to eliminate the risk of buffer overflow through dynamic adjustment of data throughput.

[0009] Step S103 of the present invention includes the following sub-steps: Step S1031, performing frame segmentation processing on the streaming data packet to extract the current data frame and the preceding reference frame; Step S1032, calculating the Hamming distance between the current data frame and the preceding reference frame at the bit logic level; Step S1033, statistically analyzing the bit flip frequency distribution characteristics of the Hamming distance to determine the characteristic perturbation offset.

[0010] The method described in this invention further includes step S107, which updates the dynamic adaptive threshold in real time based on the identification result of the deep residual network streaming data packets; wherein, if the identification result is a non-threat target, the dynamic adaptive threshold corresponding to the same area to be identified is increased to adjust the load compensation of processing resources.

[0011] Step S106 of the present invention further includes: for specific data blocks whose instruction scheduling priority exceeds the dynamic adaptive threshold, expanding the dynamic range of the feature vector through contrast stretching processing, and combining spatiotemporal consistency trajectory prediction to perform secondary correction of the safety weight score in order to preheat processing resources.

[0012] In step S102 of the present invention, the original signal is processed using asymmetric sampling logic; for the region to be identified with a security weight score higher than the preset weight threshold, the sampling frequency is increased; for the region to be identified with a security weight score lower than the preset weight threshold, the sampling frequency of the original signal is reduced.

[0013] In the fast processing channel described in this invention, the instruction dispatch engine of the central processing unit rearranges the instruction pipeline according to the instruction scheduling priority, so that high-priority streaming data packets can occupy the computing cores and vector processing units of the central processing unit first.

[0014] In step S1033 of the present invention, if the Hamming distance is less than the preset bit fluctuation threshold, the streaming data packet is determined to be a noise signal, and the data stream interception logic is triggered to prevent the noise signal from entering the subsequent deep recognition algorithm chain, thereby reducing the computational overhead of the central processing unit.

[0015] The security weight score described in this invention is distributed in a gradient according to the physical defense level of the area to be identified, and the calculation process of instruction scheduling priority amplifies the coupling gain between the security weight score, the feature disturbance offset, and the inverse of the instantaneous load rate through a nonlinear mapping function.

[0016] The fast processing channel based on L1 instruction cache prefetching described in this invention includes: preloading the feature operators associated with streaming data packets into the L1 cache of the central processing unit according to the instruction scheduling priority, so as to eliminate the addressing latency caused by the central processing unit calling data from system memory.

[0017] Compared with the prior art, the present invention has at least the following beneficial effects: Firstly, in the intelligent identification of intrusion events in graded security zones, the method of this invention realizes the logical evolution of processing resources from static allocation to dynamic perception. The method acquires the feature tensor stream of pixels to be processed in each region in real time through the processor, calculates the feature entropy variation between the current tensor frame and the historical background tensor sequence using a sliding window, and nonlinearly couples the feature entropy variation with the security weight vector in the configuration memory to generate a logical arbitration operator to drive the processor scheduling protocol. This mechanism breaks the limitation of conventional processing systems that rely solely on preset identity tags for computing power allocation, enabling processing resources to be dynamically reorganized on a microsecond scale according to the uncertainties within the data stream. This ensures that the processor always operates on the node with the most severe entropy increase in the signal, thereby eliminating the contradiction between resource idleness and instruction accumulation at the underlying logic level.

[0018] Secondly, a system-level numerical damping mechanism based on bus load feedback is constructed. The method of this invention collects the instantaneous load rate of the data bus and uses it as the denominator in the operation process of the logic arbitration operator, thereby forming an automatically adjustable data admission threshold in the processing link. When multiple concurrent data streams cause unexpected delays in the instruction pipeline, the logic arbitration operator dynamically floats down as the load rate increases, driving the scheduling engine to forcibly suspend the currently executing low-operator-level tasks and open an execution channel based on the first-level cache prefetch for high-operator-level data streams. This avoids the risk of buffer overflow caused by low-value noise signals, effectively suppresses the occurrence of systemic logic deadlock, and ensures that the system still has the ability to prioritize the parsing of high-risk data under extreme overload conditions.

[0019] Third, it achieves asymmetric gain in identification path under multi-dimensional collaboration. The method of this invention establishes a closed-loop scheduling model integrating regional value, event activity, and system load. After calculating the execution priority score, it triggers differentiated execution paths for data packets of different magnitudes. For specific data blocks with scores exceeding the dynamic adaptive threshold, the processor performs pre-enhancement processing based on contrast stretching, artificially expanding the dynamic range of feature vectors to improve the signal-to-noise ratio of subsequent algorithm matching. It also combines spatiotemporal consistency trajectory prediction to perform secondary weighting of safety weights to achieve resource preheating. This multi-mechanism coupling of feature enhancement, trajectory prediction, and queue reordering enables the system to shorten processing latency and expand the number of concurrent processing paths without increasing the hardware clock frequency, thereby enhancing the robustness of the electronic digital processing link in complex electrical signal environments. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the intrusion identification process involving feature entropy change sensing and bus load feedback, which is involved in this invention. Figure 2 This invention relates to a schematic diagram of the system logic architecture supporting instruction pipeline reordering and cache prefetching. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings.

[0022] Example 1: This example relates to an intelligent identification method for intrusion events in graded security zones, including the following steps: Step S101, Obtain the regional security weight: Obtain the security weight score of the corresponding region to be identified; Step S102, Sample and generate binary stream message: Sample the video signal sequence of the area to be identified and generate a streaming data message consisting of a binary bit stream; Step S103, extract feature perturbation offset: compare the adjacent data frames of the streaming data packet and use a logical XOR gate to extract the feature perturbation offset that represents the severity of bit flipping. Step S104, obtain bus load rate: monitor the instantaneous load rate of the data bus inside the electronic digital data processing system in real time, wherein the instantaneous load rate is determined by the proportion of the bus being occupied within a statistical unit time period; Step S105, calculate instruction scheduling priority: couple security weight score and feature disturbance offset, and introduce instantaneous load rate as denominator into weighted calculation logic to determine instruction scheduling priority that characterizes the timeliness requirements of streaming data packet processing; Step S106, drive to enable high-speed cache fast channel: compare instruction scheduling priority with dynamic adaptive threshold. When the instruction scheduling priority exceeds the dynamic adaptive threshold, drive the central processing unit to enable a fast processing channel based on the first-level instruction cache prefetch for streaming data packets, and suspend low-priority task instructions in the instruction queue. Use the central processing unit's instruction dispatch engine to rearrange the instruction pipeline to eliminate the risk of buffer overflow through dynamic adjustment of data throughput.

[0023] Step S103 in this embodiment includes the following sub-steps: Step S1031, performing frame processing on the streaming data packet to extract the current data frame and the preceding reference frame; Step S1032, calculating the Hamming distance between the current data frame and the preceding reference frame at the bit logic level; Step S1033, statistically analyzing the bit flip frequency distribution characteristics of the Hamming distance to determine the characteristic perturbation offset.

[0024] The method described in this embodiment further includes step S107, which updates the dynamic adaptive threshold in real time based on the identification result of the deep residual network streaming data packets; wherein, if the identification result is a non-threat target, the dynamic adaptive threshold corresponding to the same area to be identified is increased to adjust the load compensation of processing resources.

[0025] Step S106 in this embodiment further includes: for specific data blocks whose instruction scheduling priority exceeds the dynamic adaptive threshold, expanding the dynamic range of the feature vector through contrast stretching processing, and combining spatiotemporal consistency trajectory prediction to perform secondary correction of the safety weight score for processing resource preheating.

[0026] In step S102 of this embodiment, the original signal is processed using asymmetric sampling logic; for the region to be identified with a security weight score higher than the preset weight threshold, the sampling frequency is increased; for the region to be identified with a security weight score lower than the preset weight threshold, the sampling frequency of the original signal is reduced.

[0027] In this embodiment, within the fast processing channel, the CPU's instruction dispatch engine rearranges the instruction pipeline according to instruction scheduling priority, so that high-priority streaming data packets preferentially occupy the CPU's computing cores and vector processing units.

[0028] In step S1033 of this embodiment, if the Hamming distance is less than the preset bit fluctuation threshold, the streaming data packet is determined to be a noise signal, and the data stream interception logic is triggered to prevent the noise signal from entering the subsequent deep recognition algorithm chain, thereby reducing the computational overhead of the central processing unit.

[0029] The security weight score described in this embodiment is distributed in a gradient according to the physical defense level of the area to be identified, and the calculation process of instruction scheduling priority amplifies the coupling gain between the security weight score, the feature disturbance offset, and the inverse of the instantaneous load rate through a nonlinear mapping function.

[0030] The fast processing channel based on L1 instruction cache prefetching described in this embodiment includes: preloading the feature operators associated with streaming data packets into the L1 cache of the central processing unit according to the instruction scheduling priority, so as to eliminate the addressing latency caused by the central processing unit calling data from system memory.

[0031] Example 2: In this example, when the system faces a high-concurrency overload condition with concurrent input of heterogeneous monitoring data streams, the conventional electronic digital data processing system allocates processing resources according to a static priority protocol. This causes sudden signals from low-level areas to crowd out bus bandwidth, resulting in overflow and loss of feature packets from high-level areas in the data buffer. The intelligent identification method for intrusion events in graded security areas transforms the computing power resource allocation logic into a dynamic arbitration mechanism based on the coupling of data internal feature entropy change and system real-time state. It configures processor resources to tilt towards data streams with feature bit flipping frequency greater than the configured threshold, thus mitigating the physical computing power competition conflict between redundant sudden data and real-time computing timeliness requirements under the existing hardware unit architecture.

[0032] In the digital logic processing link, the processor obtains the security weight score of the corresponding region to be identified, samples the video signal sequence of the region to be identified and generates a streaming data packet composed of binary bit streams. For regions with security weight scores higher than the benchmark value, the sampling frequency is increased to increase the feature tensor density. The processor compares adjacent data frames of the streaming data packet and uses a logical XOR gate to extract the feature perturbation offset representing the severity of bit flipping. The processing unit calls the video decoding module to initially parse the compressed format original video stream into a baseband pixel matrix, extracts the binary sequence of specific macroblock motion vectors in the baseband pixel matrix as the comparison input, and eliminates non-physical data flipping interference caused by the underlying video coding protocol. When performing logical XOR comparison, this invention does not blindly perform calculations on the compressed full binary stream, but rather the processing unit parses and extracts the macroblock motion vector (MV) and residual data from the streaming data packet. The specific syntax element bit segments of the difference coefficient distribution are compared with the motion vector bits of macroblocks with the same spatial coordinates in adjacent frames. The XOR operation is used to detect the state flip frequency at the binary level. Since the change of motion vector maps the displacement trajectory of objects in physical space, this comparison of semantic-level metadata can penetrate the avalanche effect of entropy coding and accurately extract the feature perturbation offset that reflects the true intrusion kinetic energy of the target. The instantaneous load rate of the data bus inside the electronic digital data processing system is monitored in real time. The instantaneous load rate is determined by the proportion of the bus occupied period within a statistical unit time period. The processing engine couples the security weight score and the feature perturbation offset, and introduces the instantaneous load rate as a denominator into the weighted calculation logic to determine the instruction scheduling priority that characterizes the timeliness requirements of streaming data packet processing. Based on the basic model of multi-task real-time scheduling and queuing theory, the processing engine uses mathematical expressions to determine the priority of instruction scheduling that characterizes the timeliness requirements of streaming data packet processing. Determine priority quantifiable indicators, among which, This indicates the final output instruction scheduling priority value. This indicates that the security weight score for the corresponding region is read from the configuration storage area. This indicates that the characteristic perturbation offset calculated in the preceding steps has been normalized and restricted to a value between zero and one. The system represents the instantaneous bus load rate obtained statistically. All variables jointly define the instruction resource access boundary. Under the weighted calculation logic, the characteristic disturbance offset is injected into the static security weight score with dynamic attribute parameters. The instantaneous load rate and the aforementioned parameters are combined to construct a feedback calibration loop. When the bus faces multi-channel concurrent congestion, the generated instruction scheduling priority is adjusted downward as the bus load increases. Attenuation control is applied to non-core data streams originating from low-level areas to ensure that burst high-value data frames have priority in obtaining instructions.

[0033] The control engine continuously compares instruction scheduling priorities with dynamic adaptive thresholds. When an instruction scheduling priority exceeds the dynamic adaptive threshold, it drives the CPU to open a fast processing channel for streaming data packets based on L1 instruction cache prefetching. The operating system kernel scheduler calls low-level assembly-level data prefetch instructions, bypassing the default memory page table replacement mechanism, to forcibly lock the physical memory address segment where the target analysis operator associated with the streaming data packet is located, and prefetch it into the CPU's L1 cache. The fast processing channel does not directly load the massive raw video packets into the L1 cache, but rather uses instruction prefetching. The mechanism locks the core operators, weight coefficients, and feature descriptors required for the current message feature dimension in the deep residual network in the L1 cache. By eliminating the latency caused by the CPU addressing from system memory when executing high-priority identification tasks, the processor core can obtain calculation parameters with zero wait cycles, thereby greatly improving the instruction turnover rate of a single frame of data. This mechanism, which improves throughput from the computational side, combined with a fixed-depth circular asynchronous buffer queue in main memory, can effectively smooth bus load fluctuations and prevent buffer overflows caused by the back-end inference speed not keeping up with the front-end throughput. A fixed-depth circular asynchronous buffer queue is established in the memory as an isolation medium between the hardware bus and the deep residual network. It temporarily stores bursty high-priority streaming data packets based on the underlying hardware timestamps. A physical queuing buffer mechanism compensates for the processing time difference between microsecond-level data congestion on the bus and millisecond-level inference operations of the deep learning network. Low-priority task instructions in the instruction queue are suspended. The instruction pipeline is rearranged using the CPU's instruction dispatch engine to eliminate buffer overflow risks through dynamic adjustment of data throughput. Within this processing channel, the CPU performs contrast stretching calculations to adjust feature vectors to adapt to the signal-to-noise ratio requirements of the deep residual network algorithm. Secondary correction parameters are added to the security weight score based on the spatiotemporal trajectory prediction model to schedule computing resources in adjacent areas. The system adjusts the instantaneous load rate in real time based on the area identification results output by the deep residual network. When the identification result points to a non-threat target, a subsequent dynamic adaptive threshold is added to trigger load compensation adjustment for that area. The processing link relies on the rearranged instruction pipeline and the data access threshold floating based on load feedback to eliminate latency caused by CPU memory addressing, maintaining the stability of the digital processing system's parsing response to core risk data.

[0034] Example 3: This example measures the throughput stability and instruction scheduling timeliness of the internal data bus under overload conditions with a massive influx of streaming data packets. The experiment is built on a hardware-in-the-loop simulation platform. The central processing unit (CPU) uses a 3.2GHz chip with an integrated 512KB L1 cache. The data bus width is set to 256 bits. The original input data comes from a publicly available general dataset of security surveillance videos. Gaussian white noise with a signal-to-noise ratio of 20dB and power frequency interference harmonics at a frequency of 50Hz are superimposed on the video signal sequence of the area to be identified to simulate an industrial electromagnetic interference environment. A dynamic adaptive threshold quantization criterion is set to determine the... The strategy logic is built based on the physical limit of the data bus bandwidth. When the percentage of the data bus's occupancy period per unit time approaches the physical extreme value, the dynamic adaptive threshold shifts to the upper limit of the value range to balance the response latency of the high-security-weighted data stream and the processor's throughput load. The initial baseline value of the dynamic adaptive threshold is set to 0.65, which is derived from queuing theory when the bus is in an 80% duty cycle state. The experiment sets up three independent test channels. The first control group adopts a static priority scheduling architecture based on security weight scores. The second control group adopts a weighted calculation logic that does not use the instantaneous load rate as a denominator. The experimental group adopts an intelligent identification method that includes an instantaneous load rate feedback loop.

[0035] The test stream contains 1000 concurrent video signal sequences. 100 of these sequences correspond to areas to be identified with a security weight score of 0.9, and 900 correspond to areas to be identified with a security weight score of 0.2. The processor samples the video signal sequences and generates streaming data packets composed of binary bitstreams. The system compares adjacent data frames of the streaming data packets and uses a logical XOR gate to extract the characteristic perturbation offset representing the severity of bit flips. Under conditions of superimposed Gaussian white noise and power frequency interference, the intermediate characteristic data of the test stream is observed. The test stream with a security weight score of 0.2 produces pseudo-abnormal bit flips, and its characteristic perturbation offset increases to 0.75. The percentage of bus occupancy cycles per unit time period is monitored and statistically analyzed in real time. The instantaneous load rate of the data bus reaches 92.5%. The scheduling engine couples the security weight score and the characteristic perturbation offset, and introduces the instantaneous load rate as a denominator into the weighted calculation logic. In the experimental group, for test streams with high characteristic perturbation offsets and low security weight scores, a weighted calculation is performed. The instruction scheduling priority of the logical output converged to 0.16 under the influence of the instantaneous load rate, which is lower than the dynamic adaptive threshold of 0.65. In the second control group, due to the lack of an instantaneous load rate suppression dimension, the scheduling priority of the same test stream increased to 0.85. In the experimental group, the test stream with a security weight score of 0.9 had an instruction scheduling priority of 1.24, exceeding the dynamic adaptive threshold. This drove the CPU to open a fast processing channel based on L1 instruction cache prefetching for the streaming data packet. The system suspended low-priority task instructions in the instruction queue and rearranged the instruction pipeline using the instruction dispatch engine. Measured data showed that the response latency of the test stream with a security weight score of 0.9 in the first control group was 145.2ms, with a data buffer overflow packet loss rate of 12.4%. The response latency of the second control group was 98.6ms, with a packet loss rate of 8.5%. The response latency of the test stream with a security weight score of 0.9 in the experimental group remained at 11.2ms, with a data buffer overflow packet loss rate of 0%.

[0036] Instantaneous load rate was introduced as a gradient variable to measure the nonlinear boundary effect of the system. The data bus was controlled to operate under three load conditions: 50%, 85%, and 98%. When the instantaneous load rate increased from 85% to 98%, the instruction scheduling priority of the test stream with a security weight score of 0.2 in the test group showed a nonlinear decay, while the L1 instruction cache hit rate of the streaming data packets with a security weight score of 0.9 remained at 99.2%. When the instantaneous load rate was greater than 98%, a dynamic adaptive threshold triggered an extreme interception state, suspending the queuing of all low-priority task instructions. This calculation logic suppressed streaming data packets with abnormal characteristic perturbation offsets in a physical environment containing channel noise and high bus load, maintained the CPU's scheduling response to streaming data packets with a security weight score of 0.9, and reduced the risk of data buffer overflow.

[0037] Example 4: In this example, when the digital data processing system faces signal acquisition conditions where the high security weight score area is subject to strong light and shadow abrupt interference, the two-dimensional edge parameters extracted by the conventional image feature operator are easily affected by ambient light, resulting in data drift. This leads to abnormal intrusion probability parameters output by the deep residual network, causing abnormal adjustment of the dynamic adaptive threshold in the digital logic processing link and squeezing out the data bus bandwidth.

[0038] The system is configured with a deep residual network model based on a spatiotemporal 3D convolutional architecture. The central processing unit extracts incoming streaming data packets in continuous... The system uses feature tensors from each video sampling period to construct a four-dimensional input matrix encompassing both temporal sequence and spatial pixel array. The central processing unit (CPU) loads this four-dimensional input matrix into a residual block of a three-dimensional convolutional kernel. The residual block uses a cross-layer identity mapping bypass to obtain the fundamental edge gradient parameters of the image. The three-dimensional convolutional kernel is then used to calculate the inter-frame pixel displacement parameters in the temporal dimension. The computation module outputs a spatiotemporal feature map containing the target's kinematic properties. The system then applies a Kalman filter's state transition matrix, and based on the centroid observation coordinates of the current data frame and the prior velocity parameters of the preceding reference frame, calculates the target's position in subsequent frames. Within a given time period, the central processing unit (CPU) calculates the intersection-union ratio (IUR) between the observed and predicted trajectory bounding boxes. If the IUR value is within a consecutive time period... If the overlap value exceeds the set benchmark parameter within a sampling period, the system determines that the streaming data packet represents non-threat target activity.

[0039] The control engine obtains the intersection-union ratio (IU / U) value as the confidence multiplier. Combined with a set step size factor, it uses an exponential smoothing algorithm to correct the dynamic adaptive threshold for the next sampling period. The specific calculation relationship is as follows: ,in, For the updated dynamic adaptive threshold, The dynamic adaptive threshold for the current sampling period. This is the step size factor, and its value is set to between 0.05 and 0.15. To calculate the intersection-union ratio, the control engine uses this parameter calculation logic to increase the admission priority threshold of subsequent corresponding regional streaming data packets after determining that there is no threat event. This reduces the occupancy of the central processing unit's computing cores by redundant feature tensors generated by environmental noise, and maintains the stability of computing power allocation in the continuous sampling state of the digital instruction scheduling architecture.

[0040] Example 5: In the initial deployment scenario of a multi-level defense system in a heterogeneous industrial park, the system needs to establish a mapping benchmark between regional security dimensions and data flow access logic before accessing real surveillance video signals. The control engine reads the asset valuation parameters and historical intrusion frequency parameters of each area to be identified within the physical topology map of the park. The arithmetic logic unit applies a normalization algorithm to convert the asset valuation parameters and historical intrusion frequency parameters into dimensionless continuous variables in the range of zero to one. The arithmetic logic unit introduces a set environmental risk coefficient and multiplies the aforementioned three variables to obtain the basic security measure of each area. Based on this parameter extraction, the control engine drives the test signal generator to inject a simulated interference bit stream with a gradient increasing bandwidth occupancy rate into the internal data bus of the electronic digital data processing system. At the same time, it continuously monitors the memory occupancy status of the central processing unit's data buffer. When the first overflow and packet loss phenomenon of the data buffer is recorded, the control engine locks the unit time occupancy period ratio of the data bus at this time and records it as the reference coordinate for calibrating the critical load to quantify the physical boundary of the computing power of the current hardware architecture.

[0041] Based on the basic safety metrics obtained from previous steps, the control engine applies a classification and statistical algorithm to assign independent safety weight scores to all regions to be identified. For the initial state setting of the dynamic adaptive threshold, the control engine calculates the initial baseline value by combining the reference coordinates of the calibrated critical load. The specific calculation relationship is as follows: ,in, This serves as the initial baseline value for the dynamically adaptive threshold. The reference coordinates for calibrating the critical load, The hardware attenuation compensation factor is set to a value of 0.01 to 0.05. After the calculation is completed, the control engine writes the security weight score of all areas to be identified and the initial reference value into the non-volatile configuration register of the central processing unit. The electronic digital data processing system relies on the offline data filling and calibration procedure to generate a judgment scale that matches the physical bus throughput limit before acquiring streaming data packets, thereby eliminating the instruction scheduling timing disorder caused by the vacancy of judgment parameters during the system startup phase.

[0042] Example 6: In this example, in a security data processing node deployment scenario integrating heterogeneous computing cores, the system initiates offline calibration for the physical bandwidth of the hardware bus and the memory read / write latency of a specific batch of hardware. The control engine drives the test signal generator to generate a bandwidth increment sequence, which is then sequentially injected into the data bus with a load step size of [missing value]. The test bitstream is monitored, and the status bits of the CPU data buffer are monitored using the performance monitoring unit. When the status bit jumps from idle to the overflow warning threshold, the control engine triggers an interrupt and records the current instruction processing cycle count. The percentage of cycles occupied per unit time is calculated based on the bus operating frequency and recorded as a reference coordinate for calibrating the critical load. The arithmetic logic unit introduces a hardware attenuation compensation factor. For reference coordinates The weighted calculation is performed, and the determined judgment result serves as the physical boundary parameter for the subsequent dynamic access arbitration mechanism of the electronic digital data processing system.

[0043] After completing the hardware physical boundary calibration, the system initiates preheating and debugging of the spatiotemporal consistency trajectory prediction model for a specific defense zone. The central processing unit (CPU) acquires the statistical features of the background pixels in the defense zone and calculates the edge gradient variance distribution under different illumination intensities to establish the spatial filter weights of the feature perturbation offset. The system controls the target simulator to perform predetermined movements within the defense zone, and the CPU uses 3D convolution kernels to extract continuous... The pixel displacement tensor of each video sampling period is used, and the prior velocity component in the state transition matrix is ​​determined using a least-squares fitting algorithm. The computation unit performs an intersection-union (IoU) calculation on the predicted trajectory bounding box and the observed trajectory bounding box to determine the overlap benchmark parameter that supports the non-threat determination in the current deployment environment. The control engine then uses the obtained reference coordinates. Hardware attenuation compensation factor The overlap reference parameter is encapsulated as an initialization parameter matrix and written into a configuration register of non-volatile memory. The instruction scheduling logic of the electronic digital data processing system completes the adaptive adaptation to the physical underlying computational limits.

Claims

1. A method for intelligent identification of intrusion events in graded security zones, characterized in that, Includes the following steps: Step S101: Obtain the security weight score of the corresponding region to be identified; Step S102: Sample the video signal sequence of the area to be identified and generate a streaming data packet consisting of a binary bit stream; Step S103: Compare adjacent data frames of the streaming data packets and use a logical XOR gate to extract the feature perturbation offset that characterizes the severity of bit flipping. Step S104: Real-time monitoring of the instantaneous load rate of the internal data bus of the electronic digital data processing system, wherein the instantaneous load rate is determined by the proportion of the bus being occupied within a statistical unit time period. Step S105: Couple the security weight score and the characteristic disturbance offset, and introduce the instantaneous load rate as the denominator into the weighted calculation logic to determine the instruction scheduling priority that characterizes the timeliness requirements of streaming data packet processing; Step S106: Compare instruction scheduling priority with dynamic adaptive threshold. When instruction scheduling priority exceeds dynamic adaptive threshold, drive the central processing unit to open a fast processing channel based on L1 instruction cache prefetch for streaming data packets, and suspend low-priority task instructions in the instruction queue. Use the central processing unit's instruction dispatch engine to rearrange the instruction pipeline to eliminate the risk of buffer overflow through dynamic adjustment of data throughput.

2. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, Step S103 includes the following sub-steps: Step S1031, perform frame processing on the streaming data packet to extract the current data frame and the preceding reference frame; Step S1032, calculate the Hamming distance between the current data frame and the preceding reference frame at the bit logic level; Step S1033, statistically analyze the bit flip frequency distribution characteristics of the Hamming distance and determine the characteristic perturbation offset.

3. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, It also includes step S107, which updates the dynamic adaptive threshold in real time based on the identification results of the streaming data packets from the deep residual network; wherein, if the identification result is a non-threat target, the dynamic adaptive threshold corresponding to the same area to be identified is increased to adjust the load compensation of processing resources.

4. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, Step S106 further includes: for specific data blocks whose instruction scheduling priority exceeds the dynamic adaptive threshold, expanding the dynamic range of the feature vector through contrast stretching processing, and combining spatiotemporal consistency trajectory prediction to perform secondary correction of the safety weight score in order to preheat processing resources.

5. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, In step S102, the original signal is processed using asymmetric sampling logic; for the region to be identified with a security weight score higher than the preset weight threshold, the sampling frequency is increased; for the region to be identified with a security weight score lower than the preset weight threshold, the sampling frequency of the original signal is reduced.

6. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, Within the fast processing channel, the CPU's instruction dispatch engine rearranges the instruction pipeline according to instruction scheduling priorities, so that high-priority streaming data packets have priority to occupy the CPU's computing cores and vector processing units.

7. The intelligent identification method for intrusion events in a graded security zone according to claim 2, characterized in that, In step S1033, if the Hamming distance is less than the preset bit fluctuation threshold, the streaming data packet is determined to be a noise signal, and the data stream interception logic is triggered to prevent the noise signal from entering the subsequent deep recognition algorithm chain, thereby reducing the computational overhead of the central processing unit.

8. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, The security weight score is distributed in a gradient according to the physical defense level of the area to be identified, and the calculation process of instruction scheduling priority amplifies the coupling gain between the security weight score, the characteristic disturbance offset and the inverse of the instantaneous load rate through a nonlinear mapping function.

9. The intelligent identification method for intrusion events in a graded security zone according to claim 1, characterized in that, Enabling a fast processing channel based on L1 instruction cache prefetching includes: preloading the feature operators associated with streaming data packets into the CPU's L1 cache according to instruction scheduling priority, so as to eliminate the addressing latency caused by the CPU calling data from system memory.

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