Security robot system energy saving method based on edge calculation

Abstract

The invention discloses a security robot system energy saving method based on edge computing. Comprising the steps of environment adaptive data acquisition, heterogeneous data denoising alignment, event-driven pulse neural network processing, incremental edge model updating, energy consumption sensing task division, chip-level dynamic voltage frequency regulation and control, abnormal driving response and end-edge-cloud collaborative strategy generation. The invention belongs to the technical field of security robot systems, significantly reduces sensor data redundancy, effectively inhibits non-event scene computing resource consumption, realizes chip energy consumption dynamic adaptation and multi-robot endurance collaborative optimization, and breaks through the technical bottlenecks of a traditional scheme in the fields of dynamic perception, computing efficiency and collaborative scheduling.

Description

Technical Field

[0001] The present invention belongs to the technical field of security robot systems, and specifically refers to an energy-saving method for security robot systems based on edge computing. Background Art

[0002] Traditional security robot systems' energy-saving methods suffer from significant technical flaws. At the data acquisition level, sensors often employ a static, fixed-frequency acquisition strategy, unable to dynamically adjust the sampling rate based on factors such as ambient light intensity and target density, resulting in a large amount of redundant data. Furthermore, the heterogeneous nature of multimodal data, including lidar, infrared, and audio, complicates cross-modal denoising and spatiotemporal synchronization. Traditional filtering methods struggle to effectively eliminate mixed noise, and the calibration accuracy of multi-sensor coordinate systems is insufficient, impacting the reliability of target tracking and behavior analysis.

[0003] At the computational processing level, traditional solutions rely on a continuous, full-time computing mode, intensively processing all sensor data even in static scenarios, resulting in a significant waste of computing resources. Model updates require complete parameter transmission, making real-time iterative optimization difficult in scenarios with limited network bandwidth. At the hardware level, chip voltage and frequency use a fixed combination strategy, unable to dynamically adjust to real-time computing power requirements, resulting in a sharp drop in energy efficiency under low loads. Task scheduling uses static thresholds to divide local and edge computing tasks, which cannot adapt to load fluctuations on edge servers and can easily lead to task backlogs or idle resources.

[0004] Furthermore, multi-robot collaboration lacks global optimization capabilities. The equal weight distribution strategy causes low-power nodes to prematurely deplete their energy. The abnormal response mechanism relies on a single threshold, resulting in a high false alarm rate and a rigid response model. These issues severely restrict the system's endurance and real-time performance, necessitating innovative approaches to achieve full-link energy-saving optimization. Therefore, developing an integrated energy-saving approach based on edge computing, combining dynamic perception, sparse computing, and collaborative optimization, is an inevitable choice to overcome the bottlenecks of existing technologies. Summary of the Invention

[0005] In response to the above situation, in order to overcome the defects of the existing technology, the present invention provides an energy-saving method for a security robot system based on edge computing. In response to the technical problems in the existing energy-saving methods of security robot systems, such as the static acquisition of sensors leading to redundant data accumulation and serious spatiotemporal dislocation of cross-modal data, this solution creatively adopts reinforcement learning dynamic control and adversarial autoencoder joint optimization to achieve the technical effects of dynamic adaptation of acquisition frequency to environmental changes and precise spatiotemporal synchronization of multi-modal data; in response to the existing technical problems of full-time computing resource waste, low model update efficiency, and rigid hardware energy efficiency adaptation, this solution creatively adopts event-driven sparse computing triggering and adaptive optimization dynamic control to achieve the technical effects of zero computing resource occupancy in non-event scenarios and dynamic matching of chip energy consumption with real-time load; in response to the existing technical problems of rigid task allocation strategy, imbalance of multi-node coordination, and single abnormal response mode, this solution creatively adopts deep reinforcement learning dynamic scheduling and federated collaborative optimization to achieve the technical effects of edge load balancing optimization, hierarchical response energy consumption adaptation, and collaborative improvement of multi-robot endurance.

[0006] The present invention provides an energy-saving method for a security robot system based on edge computing, which includes the following steps:

[0007] Step S1: Environmental adaptive data collection; reinforcement learning dynamically controls the frequency of multi-sensor acquisition, and compressed sensing technology extracts sparse features;

[0008] Step S2: Heterogeneous data denoising and alignment; cross-modal joint denoising based on adversarial autoencoders, and spatiotemporal synchronization matrix calibration data;

[0009] Step S3: Event-driven spiking neural network processing; spiking neural network events are sparsely triggered, and LSTM dynamically optimizes the pulse threshold;

[0010] Step S4: Incremental edge model dynamic update; online teacher-student model distillation and differential compression to achieve incremental update;

[0011] Step S5: Energy-aware task division; based on deep reinforcement learning dual objectives, optimize task dynamic division and edge computing;

[0012] Step S6: Dynamic chip-level voltage and frequency control; adaptive optimization and control of voltage and frequency to match computing power requirements;

[0013] Step S7: Abnormal-driven adaptive response energy saving; multi-task learning shared features, hierarchical response;

[0014] Step S8: Generate a collaborative energy-saving strategy for end-edge-cloud collaboration; aggregate multi-end data through federated learning, integrate battery capacity and network delay parameters, and generate a global strategy.

[0015] Furthermore, step S1 is specifically as follows: using edge nodes to analyze light intensity, noise spectrum, and moving target density in real time, using reinforcement learning to dynamically adjust the acquisition frequency of multiple sensors (infrared radar / millimeter wave radar), combining compressed sensing technology to extract sparse features, and generating compressed data packets with timestamps; the specific steps are as follows:

[0016] S11: Real-time analysis of environmental parameters, edge nodes collect light intensity, noise spectrum, and moving target density data;

[0017] S12: Dynamic frequency adjustment, reinforcement learning algorithm dynamically controls the infrared / millimeter wave radar acquisition frequency;

[0018] S13: Sparse feature extraction, compressed sensing technology extracts sparse features of multimodal data;

[0019] S14: Data packet generation, generating a compressed data packet with a timestamp.

[0020] Furthermore, step S2 is specifically as follows: voxel filtering and denoising is performed on the lidar point cloud based on the adversarial autoencoder (AAE), improved wavelet threshold denoising is used on the audio data, a spatiotemporal synchronization matrix is ​​established to align the multi-sensor coordinate system, and a calibrated multimodal data stream is output.

[0021] Through the above steps, the space-time coordinate systems of the lidar and infrared sensor can be aligned, and the error is greatly reduced.

[0022] Furthermore, step S3 is specifically as follows: deploying a spiking neural network (SNN), triggering sparse computing only for dynamic events (such as moving heat sources and abnormal decibel mutations), dynamically adjusting the neuron pulse threshold through LSTM, suppressing redundant computing of static scenes, and outputting event feature vectors.

[0023] Furthermore, step S4 is specifically as follows: using online knowledge distillation technology, locally updating the attention layer parameters through the teacher-student model (retaining the original feature extraction layer), using differential compression to compress the model update amount to 5%-8%, and outputting lightweight model parameters.

[0024] The above steps can significantly improve the performance of the attention layer and significantly reduce the impact of redundant gradient clipping on accuracy; the model update package is significantly compressed and more suitable for network transmission.

[0025] Furthermore, step S5 is specifically as follows: constructing a dual-objective optimization strategy of energy consumption and delay based on deep reinforcement learning (DRL), dynamically dividing high-time tasks (local processing) and low-priority data (edge ​​upload) through the deep reinforcement learning algorithm, and outputting the task queue.

[0026] Furthermore, the step S6 is specifically as follows: integrating a PMU power management unit, combining an adaptive optimization algorithm to adjust the FPGA / GPU core voltage and clock frequency in real time, and outputting chip power consumption parameters.

[0027] Furthermore, step S7 is specifically as follows: based on the multi-task learning shared feature extraction layer, the threat level and type are output in parallel, a graded response (sleep / local scan / full tracking) is triggered, and a dynamic instruction is generated; the specific steps are as follows:

[0028] S71: Shared feature layer construction, shared feature extraction layer of multi-task learning framework;

[0029] S72: Threat level determination, parallel output of threat level (high / medium / low) and type (intrusion / fire / abnormal sound source);

[0030] S73: Response strategy generation, triggering three-level response instructions (sleep / local scan / full tracking) based on the threat level;

[0031] S74: Dynamic instructions are issued to generate device control instructions (motor speed / camera angle / alarm intensity).

[0032] Furthermore, step S8 is specifically as follows: aggregating multi-robot data through federated learning, integrating battery capacity and network delay parameters, and generating a global optimal strategy.

[0033] Furthermore, step S1: environmental adaptive data acquisition transmits the dynamically compressed multi-sensor data packet to step S2: heterogeneous data denoising and alignment; step S2 outputs the calibrated multimodal data stream to step S3: event-driven pulse neural network processing; step S3 triggers sparse calculation based on dynamic event feature vectors, and the generated event feature vectors are divided into two transmission paths: one path to step S4: incremental edge model dynamic update to achieve lightweight model parameter iteration, and the other path to step S7: abnormal driven adaptive response energy saving triggering hierarchical response instructions; step S5: energy consumption perception task division receives step S3 Event characteristics and step S6: Real-time power consumption parameters of chip-level dynamic voltage and frequency regulation, output dynamic task queue to edge node, and feedback regulation requirements to step S6 to optimize voltage and frequency combination; hierarchical response instruction feedback of step S7 adjusts the sensor acquisition frequency of step S1 and the pulse trigger threshold of step S3; step S8: The end-edge-cloud collaborative energy-saving strategy aggregates the task queue of step S5, the chip parameters of step S6 and the battery status of multiple robots, generates a global strategy and distributes it to all terminals, dynamically adjusts the task division weight of step S5 and the voltage and frequency baseline value of step S6, and forms a closed-loop optimization link.

[0034] Through the above steps, the system achieves full-link optimization of sensor data acquisition energy saving (S1-S2), computing and processing energy saving (S3-S6) and collaborative strategy energy saving (S7-S8).

[0035] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0036] (1) To address the problems of redundant data accumulation and spatiotemporal misalignment of cross-modal data caused by the static acquisition strategy of traditional security robot sensors, this solution uses environmental adaptive data acquisition and heterogeneous data denoising alignment, innovatively introduces reinforcement learning dynamic control and adversarial autoencoder joint optimization, and achieves dynamic adaptation of multi-sensor acquisition frequency to environmental changes and precise spatiotemporal synchronization of cross-modal data, significantly eliminating invalid data streams and improving target tracking continuity.

[0037] (2) In order to address the defects of traditional solutions such as waste of full-time computing resources, low model update efficiency, and rigid hardware energy efficiency adaptation, this solution uses event-driven pulse neural networks, incremental edge model updates, and chip dynamic voltage regulation to build a sparse computing trigger mechanism and lightweight model iteration strategy, combined with adaptive optimization of chip energy efficiency matching, to achieve core breakthroughs such as near-zero computing resource usage in non-event scenarios, doubling the model update transmission efficiency, and dynamic adaptation of chip energy consumption to real-time loads.

[0038] (3) Aiming at the pain points of rigid task allocation, unbalanced multi-node coordination, and single abnormal response mode in traditional systems, this solution innovatively designs deep reinforcement learning task division, multi-task hierarchical response, and federated collaborative optimization. Through dynamic task scheduling, threat hierarchical response, and battery capacity perception global strategy, it achieves full-link energy conservation enhancement, including edge computing load balancing optimization, abnormal response energy consumption hierarchical adaptation, and multi-robot endurance collaborative improvement. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A schematic diagram of an energy-saving method for a security robot system based on edge computing provided by the present invention.

[0040] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0042] Example 1, see Figure 1 The present invention provides an energy-saving method for a security robot system based on edge computing, comprising the following steps:

[0043] Step S1: Environmental adaptive data collection; reinforcement learning dynamically controls the frequency of multi-sensor acquisition, and compressed sensing technology extracts sparse features;

[0044] Step S2: Heterogeneous data denoising and alignment; cross-modal joint denoising based on adversarial autoencoders, and spatiotemporal synchronization matrix calibration data;

[0045] Step S3: Event-driven spiking neural network processing; spiking neural network events are sparsely triggered, and LSTM dynamically optimizes the pulse threshold;

[0046] Step S4: Incremental edge model dynamic update; online teacher-student model distillation and differential compression to achieve incremental update;

[0047] Step S5: Energy-aware task division; based on deep reinforcement learning dual objectives, optimize task dynamic division and edge computing;

[0048] Step S6: Dynamic chip-level voltage and frequency control; adaptive optimization and control of voltage and frequency to match computing power requirements;

[0049] Step S7: Abnormal-driven adaptive response energy saving; multi-task learning shared features, hierarchical response;

[0050] Step S8: Generate a collaborative energy-saving strategy for end-edge-cloud. Aggregate multi-end data through federated learning, integrate battery capacity and network delay parameters, and generate a global strategy.

[0051] Example 2: This example is based on the above example. Step S1 is specifically as follows: edge nodes analyze light intensity, noise spectrum, and moving target density in real time, dynamically adjust the acquisition frequency of multiple sensors (infrared radar / millimeter wave radar) using reinforcement learning, extract sparse features using compressed sensing technology, and generate compressed data packets with timestamps. The specific steps are as follows:

[0052] S11: Real-time analysis of environmental parameters, edge nodes collect light intensity, noise spectrum, and moving target density data;

[0053] S12: Dynamic frequency adjustment, reinforcement learning algorithm dynamically controls the infrared / millimeter wave radar acquisition frequency;

[0054] S13: Sparse feature extraction, compressed sensing technology extracts sparse features of multimodal data;

[0055] S14: Data packet generation, generating a compressed data packet with a timestamp.

[0056] The above steps replace traditional static sampling strategies, significantly reducing data redundancy while preserving key information features. By dynamically adjusting the sensor (infrared / millimeter-wave radar) acquisition frequency through reinforcement learning and combining it with compressed sensing technology to extract sparse features, the data volume can be dynamically compressed by 50%-70% (depending on ambient lighting and target density).

[0057] Example 3: This example is based on the above example, and step S2 is specifically as follows: voxel filtering and denoising are performed on the lidar point cloud based on the adversarial autoencoder (AAE), improved wavelet threshold denoising is used on the audio data, a spatiotemporal synchronization matrix is ​​established to align the multi-sensor coordinate system, and a calibrated multimodal data stream is output.

[0058] Step S2: Denoising and alignment of heterogeneous data. The specific steps are as follows:

[0059] Cross-modal adversarial denoising, the specific formula is:

[0060]

[0061] Where G p represents the point cloud generator network, which takes in noisy point cloud data and outputs the denoised point cloud;

[0062] D a represents the audio discriminator network, which distinguishes real audio from generated audio;

[0063] x represents the noisy input data (lidar point cloud / audio waveform);

[0064] z represents a latent variable, constraining its distribution to approximate the Gaussian prior;

[0065] λ KL =0.2,λ KL Represents the KL divergence weight coefficient, which controls the strength of the latent variable distribution constraint;

[0066] Time and space synchronization calibration, the specific formula is:

[0067] Where, T lidar Represents the homogeneous transformation matrix from the lidar coordinate system to the global coordinate system;

[0068] Represents the coordinates of the i-th feature point of the laser radar (three-dimensional homogeneous coordinates);

[0069] Indicates the coordinates of the infrared sensor matching points;

[0070] Through the above steps, the time and space coordinate systems of the lidar and infrared sensor can be aligned with an error of less than 0.1 meters.

[0071] Example 4: This example is based on the above example. Step S3 is specifically as follows: deploying a spike neural network (SNN), triggering sparse calculation only for dynamic events (such as moving heat sources, abnormal decibel mutations), dynamically adjusting the neuron pulse threshold through LSTM, suppressing redundant calculations in static scenes, and outputting event feature vectors.

[0072] Step S3: Event-driven spiking neural network processing, the specific steps are as follows:

[0073] Dynamic pulse trigger mechanism, the specific formula is:

[0074] Where V m (t) represents the neuron membrane potential at the current moment;

[0075] λ = 0.85, where λ represents the membrane potential attenuation factor (simulating the leakage characteristics of biological neurons);

[0076] w j Represents the synaptic weight, which indicates the importance of the input event, ranging from [0,1];

[0077] x j (t) represents the input event characteristics (such as the coordinates of the moving heat source and the sudden change value of the sound pressure level).

[0078] Compared with traditional technical solutions, this embodiment introduces LSTM dynamic optimization pulse threshold to suppress the false trigger rate to below 3%; through the above embodiment, it is possible to trigger calculation only in dynamic events (such as moving heat sources), and the computing resource usage in static scenarios is close to 0, which is 85% lower than the traditional full-time computing energy consumption.

[0079] Example 5. This example is based on the above example. Step S4 is specifically as follows: using online knowledge distillation technology, locally updating the attention layer parameters through the teacher-student model (retaining the original feature extraction layer), using differential compression to compress the model update amount to 5%-8%, and outputting lightweight model parameters.

[0080] Step S4: Incremental edge model dynamic update, the specific steps are as follows:

[0081] Online knowledge distillation, the specific formula is:

[0082] Where, L cls Represents the student model classification loss (cross entropy loss);

[0083] A stu Represents the output of the student model attention layer (updatable parameters);

[0084] A tea Represents the output of the teacher model attention layer (frozen parameters);

[0085] α = 0.7, α represents the task loss weight; β = 0.3, β represents the distillation loss weight;

[0086] Differential compression, the specific formula is:

[0087] Where, represents the attention layer parameter gradient;

[0088] k = 0.1, k means retaining the parameters of the top 10% of the absolute value of the gradient;

[0089] The above steps can achieve a 90% performance improvement in the first 10% of the gradient contribution of the attention layer, and the impact of redundant gradient clipping on accuracy is less than 1%. The model update package size is compressed from 2.3MB to 184KB (compression rate 8%), which is suitable for 4G network transmission (bandwidth occupancy <100Kbps).

[0090] Example 6. This example is based on the above example. Step S5 is specifically as follows: constructing a dual-objective optimization strategy of energy consumption and delay based on deep reinforcement learning (DRL), dynamically dividing high-time tasks (local processing) and low-priority data (edge ​​upload) through the deep reinforcement learning algorithm, and outputting the task queue.

[0091] Step S5: Energy consumption perception task division, the specific formula is as follows:

[0092]

[0093] Where, E local =k·f 3 , E local represents local computing energy consumption, k = 0.8, k represents the chip energy efficiency constant, and f represents the chip frequency;

[0094] D edge =C / (F edge (1-L)), D edge represents edge processing latency, C represents task computational load; F edge Edge server frame rate;

[0095] θ represents the edge load indicator function, which takes the value of 1 when the load is ≥80%;

[0096] ω1=0.6, ω1 represents the energy consumption weight; ω2=0.3, ω2 represents the delay weight; ω3=0.1, ω3 represents the load penalty weight.

[0097] This embodiment implements a real-time trade-off strategy for a dual-objective optimization framework (energy consumption and latency), avoiding edge server overload caused by traditional static threshold allocation. This embodiment can dynamically divide local and edge tasks through deep reinforcement learning (DRL), reducing task queue processing latency by 30% and improving edge load balancing by 50%.

[0098] Embodiment 7: This embodiment is based on the above embodiment, and step S6 is specifically: integrating a PMU power management unit, combining an adaptive optimization algorithm to adjust the FPGA / GPU core voltage and clock frequency in real time, and outputting chip power consumption parameters.

[0099] Step S6: Chip-level dynamic voltage and frequency control, adaptive optimization algorithm, the specific formula is as follows:

[0100]

[0101] Where V represents the chip core voltage, ranging from 0.8V to 1.2V;

[0102] f represents the chip clock frequency, ranging from 200MHz to 1.2GHz;

[0103] FPS means real-time calculation frame rate;

[0104] Temperature constraint: Chip temperature ≤ 85°C.

[0105] Example 8: This example is based on the above example. Step S7 is specifically as follows: Based on the multi-task learning shared feature extraction layer, the threat level and type are output in parallel, a graded response (sleep / local scan / full tracking) is triggered, and a dynamic instruction is generated. The specific steps are as follows:

[0106] S71: Shared feature layer construction, shared feature extraction layer of multi-task learning framework;

[0107] S72: Threat level determination, parallel output of threat level (high / medium / low) and type (intrusion / fire / abnormal sound source);

[0108] S73: Response strategy generation, triggering three-level response instructions (sleep / local scan / full tracking) based on the threat level;

[0109] S74: Dynamic instructions are issued to generate device control instructions (motor speed / camera angle / alarm intensity).

[0110] This embodiment can dynamically adjust the sensor acquisition frequency by command feedback (such as adjusting it to 20% of the base frequency in sleep mode), realize multi-task learning and share feature extraction layers, improve the threat judgment accuracy by 15%, and adapt the response energy consumption by level (the energy consumption difference between sleep and full tracking mode is up to 10 times).

[0111] Embodiment 9: This embodiment is based on the above embodiment, and step S8 is specifically: aggregating multi-robot data through federated learning, integrating battery capacity and network delay parameters, and generating a global optimal strategy.

[0112] Step S8: Generate the end-edge-cloud collaborative energy-saving strategy. The specific formula for federated learning aggregation is as follows:

[0113]

[0114] Where B k represents the remaining battery capacity of the kth robot, normalized to [0,1];

[0115] An index of 1.5 means that the weight of high-power nodes is strengthened, extending the battery life of low-power robots;

[0116] The global strategy is the output device frequency f cmd =min(f max , 0.6·f max ·Bk).

[0117] This embodiment breaks the drawbacks of traditional equal weight distribution. By aggregating the battery status of multiple robots through federated learning, a global strategy is implemented to extend the battery life of low-battery nodes by 25% (by strengthening the task allocation of high-battery nodes through exponential weighting).

[0118] Example 10, based on the above example, step S1: environmental adaptive data acquisition transmits the dynamically compressed multi-sensor data packet to step S2: heterogeneous data denoising and alignment; step S2 outputs the calibrated multimodal data stream to step S3: event-driven pulse neural network processing; step S3 triggers sparse calculation based on dynamic event feature vectors, and the generated event feature vectors are divided into two transmission paths: one path to step S4: incremental edge model dynamic update to achieve lightweight model parameter iteration, and the other path to step S7: abnormal driven adaptive response energy saving triggering hierarchical response instructions; step S5: energy consumption perception task division reception Receive the event characteristics of step S3 and step S6: real-time power consumption parameters of chip-level dynamic voltage and frequency regulation, output the dynamic task queue to the edge node, and at the same time feedback the regulation requirements to step S6 to optimize the voltage and frequency combination; the hierarchical response instruction feedback of step S7 adjusts the sensor acquisition frequency of step S1 and the pulse trigger threshold of step S3; step S8: end-edge-cloud collaborative energy-saving strategy aggregates the task queue of step S5, the chip parameters of step S6 and the battery status of multiple robots, generates a global strategy and distributes it to all terminals, dynamically adjusts the task division weight of step S5 and the voltage and frequency baseline value of step S6, and forms a closed-loop optimization link.

[0119] Through the above steps, the system achieves full-link optimization of sensor data acquisition energy saving (S1-S2), computing and processing energy saving (S3-S6) and collaborative strategy energy saving (S7-S8).

[0120] The breakthrough effect of this solution compared with the traditional solution is shown in Table 1 below:

[0121] index Traditional solution Solution of the present invention Improvement Data redundancy rate 60％-80％ 15％-25％ 70％↓ Model update transmission volume Full parameters (2.3MB) Differential compression (184KB) 92％↓ Static scene computing energy consumption Continuous full-time computing Approaching 0 100％↓ Multi-robot endurance balance Standard deviation 2.1 hours Standard deviation 0.7 hours 66％↑ Threat response false positive rate 18％-25％ 3％-5％ 80％↓

[0122] Table 1: Breakthrough effects of this solution compared to traditional solutions

[0123] From the above analysis, it can be seen that this solution achieves full-link energy-saving breakthroughs in the three major aspects of data collection, computing and processing, and collaborative scheduling through cross-step collaborative optimization and innovative technology integration, and has significant creative technological progress.

[0124] The present invention dynamically regulates the acquisition frequency of multiple sensors through reinforcement learning, and combines adversarial autoencoders to achieve spatiotemporal synchronization calibration of cross-modal data. It uses a pulse neural network to trigger a sparse computing mechanism to suppress redundant processing in static scenarios, and implements lightweight model iteration based on online knowledge distillation technology. It uses an adaptive optimization algorithm to dynamically match chip voltage frequency with real-time computing power requirements, and constructs a deep reinforcement learning dual-objective optimization framework to achieve dynamic division of edge tasks. It achieves threat classification response through multi-task learning shared features, and combines federated learning to aggregate multi-node data to generate a global collaborative strategy. The present invention significantly reduces sensor data redundancy, effectively suppresses computing resource consumption in non-event scenarios, achieves dynamic adaptation of chip energy consumption and collaborative optimization of multi-robot endurance, and breaks through the technical bottlenecks of traditional solutions in the fields of dynamic perception, computing efficiency, and collaborative scheduling.

[0125] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0126] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

[0127] The present invention and its embodiments are described above. This description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if a person skilled in the art is inspired by this and, without departing from the purpose of the present invention, designs structures and embodiments similar to this technical solution without inventiveness, they shall fall within the scope of protection of the present invention.

Claims

1. An energy-saving method for a security robot system based on edge computing, characterized by: The method comprises the following steps: Step S1: Environmental adaptive data collection; reinforcement learning dynamically controls the frequency of multi-sensor acquisition, and compressed sensing technology extracts sparse features; Step S2: Heterogeneous data denoising and alignment; cross-modal joint denoising based on adversarial autoencoders, and spatiotemporal synchronization matrix calibration data; Step S3: Event-driven spiking neural network processing; spiking neural network events are sparsely triggered, and LSTM dynamically optimizes the pulse threshold; Step S4: Incremental edge model dynamic update; online teacher-student model distillation and differential compression to achieve incremental update; Step S5: Energy-aware task division; based on deep reinforcement learning dual objectives, optimize task dynamic division and edge computing; Step S6: Dynamic chip-level voltage and frequency control; adaptive optimization and control of voltage and frequency to match computing power requirements; Step S7: Abnormal-driven adaptive response energy saving; multi-task learning shared features, hierarchical response; Step S8: Generate a collaborative energy-saving strategy for end-edge-cloud collaboration; aggregate multi-end data through federated learning, integrate battery capacity and network delay parameters, and generate a global strategy.

2. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: Step S1 specifically comprises: analyzing light intensity, noise spectrum, and moving target density in real time through edge nodes, dynamically adjusting the multi-sensor acquisition frequency using reinforcement learning, extracting sparse features in combination with compressed sensing technology, and generating compressed data packets with timestamps; The specific steps are as follows: S11: Real-time analysis of environmental parameters, edge nodes collect light intensity, noise spectrum, and moving target density data; S12: Dynamic frequency adjustment, reinforcement learning algorithm dynamically controls the infrared / millimeter wave radar acquisition frequency; S13： Sparse feature extraction: compressed sensing technology extracts sparse features of multimodal data; S14: Data packet generation, generating a compressed data packet with a timestamp.

3. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S2 specifically comprises: performing voxel filtering and denoising on the lidar point cloud based on the adversarial autoencoder, applying improved wavelet threshold denoising to the audio data, establishing a spatiotemporal synchronization matrix to align the multi-sensor coordinate system, and outputting a calibrated multimodal data stream.

4. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S3 is specifically as follows: deploying a spiking neural network, triggering sparse computation only for dynamic events, dynamically adjusting neuron pulse thresholds through LSTM, suppressing redundant computations for static scenes, and outputting event feature vectors.

5. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S4 is specifically as follows: using online knowledge distillation technology, locally updating the attention layer parameters through the teacher-student model, compressing the model update amount using differential compression, and outputting lightweight model parameters.

6. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S5 is specifically as follows: constructing a dual-objective optimization strategy of energy consumption and delay based on deep reinforcement learning, dynamically dividing high-priority tasks and low-priority data through the deep reinforcement learning algorithm, and outputting a task queue.

7. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S6 specifically includes: integrating a PMU power management unit, combining an adaptive optimization algorithm to adjust the FPGA / GPU core voltage and clock frequency in real time, and outputting chip power consumption parameters.

8. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S7 is specifically: based on the multi-task learning shared feature extraction layer, outputting the threat level and type in parallel, triggering a graded response, and generating dynamic instructions; the specific steps are as follows: S71: Shared feature layer construction, shared feature extraction layer of multi-task learning framework; S72: Determine threat level and output threat level and type in parallel; S73: Response strategy generation, triggering three-level response instructions based on the threat level; S74: Dynamic instructions are issued to generate device control instructions.

9. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S8 is specifically as follows: aggregating multi-robot data through federated learning, integrating battery capacity and network delay parameters, and generating a global optimal strategy.

10. The energy-saving method for a security robot system based on edge computing according to claim 1, characterized in that: The step S1: environment adaptive data acquisition transmits the dynamically compressed multi-sensor data packet to step S2: heterogeneous data denoising and alignment; Step S2 outputs the calibrated multimodal data stream to step S3: event-driven pulse neural network processing; step S3 triggers sparse computing based on the dynamic event feature vector, and the generated event feature vector is transmitted in two ways: one to step S4: incremental edge model dynamic update to achieve lightweight model parameter iteration, and the other to step S7: abnormality-driven adaptive response energy saving triggers hierarchical response instructions; step S5: energy consumption perception task division receives the event characteristics of step S3 and step S6: chip-level dynamic voltage and frequency regulation real-time power consumption parameters, outputs the dynamic task queue to the edge node, and at the same time feeds back the regulation demand to step S6 to optimize the voltage and frequency combination; the hierarchical response instruction feedback of step S7 adjusts the sensor acquisition frequency of step S1 and the pulse trigger threshold of step S3; step S8: the end-edge-cloud collaborative energy-saving strategy aggregates the task queue of step S5, the chip parameters of step S6 and the battery status of multiple robots, generates a global strategy and distributes it to all terminals, dynamically adjusts the task division weight of step S5 and the voltage and frequency baseline value of step S6, and forms a closed-loop optimization link.

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