59 results about "Edge analytics" patented technology

The invention discloses a traffic incident detector system based on a domestic intelligent DSP and a detection method. The system comprises a video acquisition unit, a data exchange and storage unit, an intelligent processing core and a communication interaction unit. The intelligent processing core adopts a heterogeneous SoC chip integrating a multi-core DSP, an NPU and an FFT hardware acceleration module, the FFT module is responsible for video frame frequency domain enhancement, the NPU runs a deep learning model to carry out target detection, and the multi-core DSP executes multi-target parallel tracking and event judgment. And the data exchange and storage unit supports direct carrying of data among the modules through a unified physical address space and a DMA mechanism. The method comprises the following steps: carrying out NPU target detection on a video subjected to environment self-adaptive preprocessing; a multi-core DSP is used for tracking a target in parallel and judging an event; and finally, alarm information is generated and reported. Through heterogeneous hardware division and software and hardware cooperation, high-real-time and high-precision edge analysis is realized, and the detection reliability is improved.

An anthropomorphic AI control system including a plurality of sensors configured to collect industrial input data. The control system also includes an artificial intelligence-enabled edge-deployed computing device configured to analyze and fuse multimodal input data and generate an output through anthropomorphic computing. The computing device includes a cognitive module performing real-time decision-making at the sensor edge and a controller to manage industrial process actuators across operational domains. In management of industrial fluid flow, the control system may include AI-enabled fluid characterization modules to calculate the Reynolds number and incorporate compliance with AGA3 and AGA8 standards.

The invention relates to a distribution line power factor real-time monitoring and collaborative optimization system and method, and belongs to the technical field of distribution line energy efficiency management systems and methods. According to the technical scheme, the system comprises a multi-source data acquisition and preprocessing module, an edge node calculation and edge analysis module, a power factor real-time analysis and state evaluation module, a multi-subject collaborative optimization decision module and a dynamic reactive compensation control execution module. An edge and cloud data interaction and full life cycle management module; and an abnormity early warning and fault diagnosis module. The method has the beneficial effects that the line network loss and the operation and maintenance cost are reduced at the same time, the fault-tolerant capability is achieved, the modular design supports multi-scene expansion, the method not only adapts to the process requirements of vanadium-titanium steel rolling, smelting and the like, but also can be flexibly applied to other high-load industrial 11kV lines, and the power factor regulation and control effectiveness and the system full-life-cycle benefits are improved.

The invention relates to the technical field of atmospheric environment monitoring, and discloses a sand and dust path monitoring system, which is characterized by comprising a data acquisition module, an image processing module, a feature extraction module, a path fitting module, a path fusion module and a geographic mapping module which are sequentially cascaded, the data acquisition module is used for receiving remote sensing data of a stationary meteorological satellite and generating a multi-time sand and dust monitoring binary image; the image processing module performs edge detection and morphological operation on the sand and dust monitoring binary image to realize accurate segmentation of a sand and dust region; the feature extraction module comprises a minimum enclosing rectangle calculation unit and an edge analysis unit and is used for extracting four types of feature points of the sand and dust area, and the four types of feature points comprise the lower right corner, the upper right corner, the right side line and the lower side line. According to the invention, by fusing edge detection, morphological operation and multi-feature endpoint identification, precise segmentation of a complex dust region and stable capture of motion features are realized, and the problem that feature points are easy to drift in a traditional method is effectively solved.

The invention discloses an intelligent bus duct and cable online monitoring system for wireless dual-mode collaborative networking, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing the monitoring data of a bus duct and a cable; the feature extraction module is used for performing normalization and feature cascade processing on the preprocessed monitoring data to generate monitoring feature data; the data encapsulation and communication module is used for encapsulating the monitoring characteristic data into a standardized data frame and transmitting the standardized data frame through a main communication link; the gateway convergence module is used for generating gateway convergence data; the edge analysis module is used for receiving gateway aggregation data, inputting the gateway aggregation data into an improved MFDFA-XGBoost fusion model for state judgment and outputting a state result; the cloud diagnosis module is used for receiving the state result and the monitoring feature data and generating and storing event records; and the alarm and operation and maintenance module is used for generating alarm information and realizing real-time monitoring and active early warning of the bus duct and the cable.

The invention relates to the field of intelligent inspection of substation equipment, discloses a substation equipment inspection path planning system based on an unmanned aerial vehicle, and aims to solve the problems that high-definition images are difficult to return in real time in low-frequency transmission under strong electromagnetic interference of a substation, and path planning is single. The system comprises five core modules, an equipment three-dimensional digital reference and feature library is constructed, new equipment is adapted in combination with operator similarity migration, and the observation pose is accurately reproduced to guarantee the image comparison precision; after airborne edge analysis, selectively returning data, and adapting to a low-frequency transmission link; and according to equipment similarity in batches, planning a global optimal path by fusing the path length and the flight risk. The system realizes efficient transmission of inspection data, is accurate and reliable in detection, balances the inspection efficiency and the flight safety, and meets the core requirements of safe, efficient and accurate inspection of the transformer substation.

The invention relates to the technical field of power system equipment monitoring, in particular to a substation equipment state monitoring embedded edge analysis system, which comprises a monitoring front-end module for acquiring and transmitting substation equipment operation state data; the edge processing module comprises a multi-scale wavelet transform unit, a topology continuity analysis unit and a manifold learning unit, wherein the multi-scale wavelet transform unit decomposes data and separates signals and noise through a self-adaptive threshold mechanism; the topological persistence analysis unit maps the de-noised signal to a high-dimensional feature space, calculates a persistent coherence group, and extracts a topological feature vector; and the manifold learning unit constructs a nonlinear manifold structure, calculates a geodesic distance, and executes discharge type identification and anomaly evaluation, and the FPGA and ARM heterogeneous calculation architecture is adopted, and through the synergistic effect of multi-scale wavelet transform, topology persistence analysis and manifold learning, high-precision extraction of weak discharge signals and accurate identification of fault modes are realized.

The invention specifically relates to the technical field of real-time fault diagnosis, and discloses an intelligent ship equipment state monitoring and fault diagnosis system, which comprises a data acquisition processing module, an edge analysis reasoning module, a local decision execution module, a cloud edge comprehensive analysis module, an optimization strategy making module and a man-machine interaction feedback module, the data acquisition and processing module is used for constructing a ship equipment state feature set; the edge analysis reasoning module is used for outputting a ship equipment state analysis result; the local decision execution module is used for generating a preliminary decision instruction; the cloud edge comprehensive analysis module is used for judging the ship equipment health level; the optimization strategy making module is used for making a global equipment optimization strategy; the man-machine interaction feedback module is used for providing an equipment state panel; by constructing a cloud edge collaborative model optimization mechanism, a global ship equipment health state evaluation model is continuously trained and optimized based on real-time feature data and historical data, and the real-time performance of fault response and the dynamic adaptability of state real-time perception are improved.

The invention discloses a tea fermentation degree identification method, system and device and a storage medium, and the method comprises the steps: inputting a to-be-identified tea fermentation image into a tea fermentation identification network, carrying out the feature extraction of the to-be-identified tea fermentation image through a first processing layer, and obtaining a first semantic feature map; performing multi-scale processing and edge enhancement processing on the to-be-identified tea fermentation image through an edge analysis and enhancement module layer to obtain a detail feature map; performing global dependency modeling processing through a second processing layer according to the first semantic feature map and the detail feature map to obtain a second semantic feature map; and performing classification identification on the second semantic feature map through a classification decision layer to obtain a fermentation degree identification result of the to-be-identified tea leaf fermentation image, thereby enhancing identification of image edge and texture detail features and establishing a cross-regional global context dependency relationship. Cooperative perception of local microscopic change and overall uniformity in the fermentation process is achieved, and then the identification accuracy of the tea fermentation degree is improved.

The invention belongs to the technical field of power system intelligent operation and maintenance, and discloses a power distribution operation and maintenance intelligent inspection system fused with edge calculation. A terminal data preprocessing unit; distributed edge analysis nodes; the nodes are interconnected with a communication link; an inspection task scheduling platform; a defect alarm generator; and a work order distribution and maintenance guide center. The surface temperature and partial discharge pulse signals of the power distribution equipment are obtained through the field inspection sensing device, time sequence alignment of multi-source data is achieved through time stamps in a unified format, normalization processing and trend modeling analysis are carried out in the terminal data preprocessing unit, and the system adopts a temperature change rate formula and a data normalization formula, so that the accuracy of the system is improved. Standardized conversion and dynamic index extraction are carried out on continuous time domain data, so that the recognition capability of early-stage heating hidden dangers and abnormal discharge symptoms is remarkably improved, and the misjudgment risk caused by data scale inconsistency or time dislocation is reduced.

The invention discloses a traffic video edge analysis system based on Yolov5-Edge and Prewitt algorithms, and the system achieves the real-time collection, intelligent recognition and encrypted transmission of a vehicle-mounted video through the construction of a layered architecture of an object-side equipment layer, an edge calculation layer and a cloud service layer. According to the system, a lightweight Yolov5-Edge model and a Prewitt edge detection algorithm are fused in edge equipment, multi-channel video parallel processing is realized under finite computing power, and the video inspection efficiency is effectively improved; the frame rate and the number of processing paths are dynamically adjusted through the resource monitoring and load control module, and the real-time performance and stability of AI reasoning are guaranteed; a data management and encryption mechanism is adopted, only abnormal data are stored at an edge end and clouded through RSA and AES encryption modes, and security and compliance of sensitive behavior information in the transmission and storage process are ensured. The system can identify and classify abnormal behaviors of drivers and passengers without depending on a high-performance server.

The invention relates to the technical field of bridge structure health monitoring, and discloses an urban bridge sidewalk multi-mode vibration monitoring and early warning system, which comprises a vibration sensing execution unit, an MEMS (Micro Electro Mechanical System) three-axis acceleration sensor with a built-in temperature compensation sheet, and a vibration detection unit, and is characterized in that the MEMS three-axis acceleration sensor is packaged in a 304 stainless steel base with a rubber damping pad; the self-adaptive data acquisition and transmission unit is connected with a LoRa wireless node of the vibration sensing execution unit; the edge analysis execution unit is used for receiving an embedded AI chip of data of the acquisition and transmission unit and executing vibration spectrum fingerprint matching and comfort level rating operation; and the early warning output execution unit is connected with an audible and visual alarm and a 4G communication module of the edge analysis unit. Through an embedded AI chip of the edge analysis unit, a 1 / 3 octave frequency spectrum is generated in real time and is matched with a fingerprint database, progressive early warning is realized through sound-light alarm and 4G communication, digital mapping of sidewalk vibration characteristics and pedestrian perception is established for the first time, and the defect of lack of a correlation model in the prior art is overcome.

The application provides a conference picture abnormality processing method and system based on video semantic understanding, and relates to the technical field of video processing. The method comprises the following steps: dynamically grouping a plurality of edge analysis nodes to obtain P high-availability backup groups; taking a dynamic semantic vector as a semantic matching benchmark to position a first video acquisition device to perform gradual switching from an initial display picture to a semantic adaptive picture; starting a signal layer pixel-level monitoring function to perform group internal multi-node collaborative disaster recovery; performing frame-level semantic incremental analysis to obtain a plurality of incremental semantic feature vectors to perform signal content bimodal health closed loop until a conference termination instruction is triggered. The technical problem that in the prior art, video scheduling modes are mostly based on fixed round patrol strategies or manual experience to perform picture scheduling, lack semantic understanding ability for video content itself, and cause obvious misalignment between conference semantic content and on-site display pictures, thereby reducing information presentation accuracy in the process of emergency command is solved.

The invention discloses a mining pipeline leakage positioning and intelligent valve closing system based on 5G + AI. The mining pipeline leakage positioning and intelligent valve closing system comprises a multi-mode sensor deployed at a pipeline key node, a mining 5G communication network, a cloud edge collaborative analysis platform and an intelligent execution unit. The sensor collects vibration and pressure waveform data, and the vibration and pressure waveform data are synchronously transmitted through a 5G network in a low-delay manner; the edge analysis unit is internally provided with a lightweight AI model for leakage initial judgment and triggering; the cloud analysis unit performs fusion analysis on multi-node synchronous data by adopting a space-time convolutional neural network to realize sub-wavelength level accurate positioning and intensity evaluation of a leakage point; and the decision engine generates an intelligent instruction containing a dynamic valve closing strategy according to the positioning and evaluation result, and issues the intelligent instruction to the intelligent valve for execution through the 5G network. The problems that a traditional method is poor in positioning precision, lagged in response and high in false alarm rate are solved, rapid sensing, accurate positioning, intelligent decision making and self-adaptive optimization of leakage events are achieved, and the safety and reliability of the mining liquid supply pipeline are remarkably improved.

This invention discloses a high-precision detection method for concave and convex feature points based on multi-segment straight line edge analysis, comprising the following steps: S10, image acquisition; S20, preprocessing and edge detection; S30, straight line segment extraction and filtering; S40, line segment relationship analysis and concave / convex hypothesis generation; S50, hypothesis verification and precise positioning; S60, outputting the coordinates of concave and convex points. This invention overcomes the dependence on continuous and complete contours by directly detecting and filtering multi-segment straight line edges related to potential concave and convex features from the image. Based on this, it robustly and accurately locates the coordinates of convex and concave points by analyzing the spatial topological relationships and geometric constraints between these straight line segments.

The invention relates to the technical field of intelligent safety monitoring, and discloses a wireless intelligent smoke sensing and power utilization safety cooperative monitoring network system, which comprises a plurality of multi-mode sensing units configured by intelligent sensing nodes in a distributed manner and used for synchronously acquiring chemical fingerprints, electromagnetic acoustics and environmental data; an edge processor performs edge analysis through a preset AI model, generates and broadcasts a local event vector after identifying a persistent or mutable risk, performs multi-source information cross validation by calculating a regional evidence factor, and evaluates a quantized regional threat level in combination with local confidence. The gateway device may also perform hierarchical intervention according to threat levels, and predict a risk propagation path based on an architectural space environment map model. Through combination of edge intelligence and center cooperation, high-precision identification of early-stage hidden fire disasters is realized, the false alarm rate is significantly reduced through multi-source information cross validation, and the reliability of early warning and the accuracy of intervention are improved.

The application discloses a kind of based on cloud edge collaborative processing's internet of things information management system, belong to internet of things information management technical field, by real-time collection internet of things data to sensing terminal, and transmit internet of things data to edge end;Edge end is preprocessed to the internet of things data received, obtains standardized internet of things data;Real-time determination edge analysis task, according to edge analysis task call corresponding built-in task analysis mode is analyzed to standardized internet of things data, obtains edge task analysis result;Standardized internet of things data and edge task analysis result are screened, obtain high-value data, abnormal data and data abstract, and high-value data, abnormal data and data abstract are sent to cloud end;Cloud end is according to the high-value data, abnormal data and data abstract received task analysis processing;And sensing terminal is collected parameter management.

The invention discloses an edge gateway service dynamic control system, which belongs to the technical field of edge gateways and comprises a cloud management platform and an edge end. The cloud management platform comprises a target analysis module; the edge end comprises a monitoring module, an edge analysis module and a control module; the target analysis module is used for carrying out real-time analysis on the control equipment to obtain edge analysis targets of the control equipment, carrying out strategy analysis according to the edge analysis targets to obtain target control strategies of the edge analysis targets, and sending the edge analysis targets and the target control strategies to the edge analysis module; the monitoring module is used for monitoring the control equipment in real time to obtain monitoring data; the edge analysis module is used for performing control analysis, analyzing the monitoring data according to a target control strategy and edge analysis targets, obtaining control information of each edge analysis target, and sending the control information to the control module; and the control module is used for controlling the control equipment according to the received control information.

The invention relates to the technical field of intelligent automobiles and automobile safety, in particular to a driving emotion intervention and control system based on multi-modal perception, which comprises a data acquisition module used for synchronously acquiring multi-source heterogeneous data; the edge analysis module is used for carrying out preprocessing, feature extraction and parallel analysis on the multi-source heterogeneous data so as to output an emotion probability value and passenger existence confidence; the scenarized decision-making module is used for constructing a hierarchical decision-making model based on the emotion probability value and the passenger existence confidence so as to complete emotion state judgment and driving scene classification; according to the system and the method, the emotion of the driver is accurately and comprehensively perceived, layering, grading and humanization can be executed based on deep scene understanding, and a forced safety bag is achieved.

The invention discloses an intelligent inspection system for insulator appearance imaging based on an AOV camera and an operation method. The intelligent inspection system comprises a sensing acquisition unit, an edge analysis unit and a platform management unit, the sensing acquisition unit is arranged on a tower cross arm or a transformer substation framework, the sensing acquisition unit is composed of a low-power-consumption AOV camera, an inertial sensor module, a GPS / Beidou module, a cleaning module and a power supply module, the GPS / Beidou module obtains a spatial position, and the inertial sensor module unit senses a camera attitude angle; the edge analysis unit is arranged in the camera or is used as an independent device to be installed on a tower nearby, and is used for identifying and positioning equipment such as an insulator, a lightning arrester and a wire clamp in an image; the platform management unit is arranged on a cloud platform or a data center; according to the invention, unmanned intelligent inspection is realized, through continuous sensing and intelligent triggering mechanisms of the AOV camera and in combination with a special AI algorithm, full-automatic identification of electrical equipment appearance defects in 24 hours after the whole day is realized, and manual intervention is not needed.

The application discloses a kind of intelligent garden irrigation control methods, it is related to garden irrigation technical field, and its technical solution points are as follows: including the following steps: soil humidity, temperature, net radiation are collected by soil parameter detection module;Wind speed, light intensity and future rainfall prediction are obtained by weather data acquisition module;Dynamic irrigation demand index is calculated based on edge analysis module output;Pressure compensation module generates valve control instruction based on pipe network position analysis.The application can accurately irrigate gardens according to actual needs by collecting multiple data and calculating dynamic irrigation demand index with edge analysis module;By setting pressure compensation module, the actual flow size of each position of pipe network can be dynamically calculated, the flow size of pipe network is adjusted, and the flow difference of each part of pipe network can be reduced, so that the irrigation of each part of garden is more comprehensive and accurate.

The invention relates to the cross technical field of artificial intelligence and intelligent agriculture, discloses a poultry motion distribution analysis and feed feeding method and system integrated with intelligent sensing, and aims to solve the problems of high decision delay, high misjudgment rate and resource waste caused by dependence on indirect behavior inference in the prior art. According to the method, poultry breast muscle temperature, micro-macro motion and identity data are collected in real time through a multi-mode sensing terminal, a group physiological state space-time matrix is constructed at an edge gateway, and starvation state distribution is predicted based on a hidden Markov model; and the distributed feeder autonomously executes accurate feeding according to the locally received probability graph and the dynamic threshold value. The system comprises a sensing terminal, an edge computing gateway and an intelligent feeding actuator, and a closed-loop architecture with in-situ sensing, edge analysis and autonomous response is formed. According to the application, low-delay and high-precision response to the real hunger demand of the poultry is realized, the feeding efficiency is remarkably improved, and the energy consumption and the communication overhead are reduced.

The invention discloses an activity authenticity verification method and device based on multi-modal space-time fusion, and relates to the field of computer vision. The method comprises the steps of obtaining a to-be-audited image after edge analysis node preliminary screening; according to the image content dimension, the physical environment dimension and the metadata dimension, multi-modal evidence features of the to-be-audited image are extracted, feature fusion is carried out on the multi-modal evidence features of the to-be-audited image, and the multi-dimensional evidence confidence of the to-be-audited image is obtained; and determining an activity authenticity decision result of the to-be-audited image according to the multi-dimensional evidence confidence of the to-be-audited image and the false business image identification rule. According to the technical scheme provided by the embodiment of the invention, the authenticity of the activity in the image can be accurately identified, and the manual auditing pressure is greatly relieved.

The application discloses a kind of wind farm equipment fault intelligent diagnosis method and system, the multidimensional sensor data of wind farm equipment is collected;These data are preliminarily screened using edge analysis algorithm, determine the data that need further diagnosis;The screened data is input into the pre-constructed fault diagnosis model, in the pre-constructed fault diagnosis model, data first pass through multimodal deep learning model fusion, then use transfer learning and domain adaptation and multi-agent reinforcement learning framework to determine the area where fault occurs;At the same time, the causal reasoning engine and the neural symbolic reasoning module enhanced by knowledge graph are responsible for in-depth processing and diagnosing faults.Finally, the determined fault area and diagnosis result are input into the dynamic system health index calculation module to generate the final fault diagnosis result.This method significantly improves the diagnosis efficiency and accuracy of wind farm equipment fault, and provides a strong guarantee for the stable operation of wind farm.

The application discloses an intelligent pipe network leakage detection method based on edge computing and relates to the technical field of pipe network monitoring and leakage detection.The method comprises the following steps: an edge analysis terminal extracts a pressure stability coefficient, a sound wave correlation coefficient and a flow mutation coefficient from a pretreatment data set, and inputs the pressure stability coefficient, the sound wave correlation coefficient and the flow mutation coefficient into a leakage probability evaluation model to output a leakage risk index; and an edge decision terminal generates a leakage level signal according to the leakage risk index and triggers a positioning instruction.The application extracts the pressure stability coefficient, the sound wave correlation coefficient and the flow mutation coefficient through the edge analysis terminal, inputs the pressure stability coefficient, the sound wave correlation coefficient and the flow mutation coefficient into the leakage probability evaluation model to output the leakage risk index, realizes accurate quantitative evaluation of the leakage risk, and enables the model to be self-adaptive to different working conditions by introducing dynamic weights, thereby improving the adaptability and accuracy of the evaluation.

The application is suitable for the technical field of distribution network detection, and provides a local partial discharge integrated live detection system for overhead lines of a distribution network based on an ubiquitous Internet of Things architecture, which comprises a pulse signal detection end, an edge gateway and a cloud monitoring platform; the pulse signal detection end is used for detecting partial discharge pulse signals of overhead lines and transmitting the partial discharge pulse signals to the edge gateway; the edge gateway comprises a synchronous pulse trigger module, a time calibration module and an edge analysis module; the edge analysis module is used for generating a positioning distribution map of partial discharge points; the cloud monitoring platform comprises a multi-end data fusion module, a risk assessment module and a result feedback module; the cloud monitoring platform is used for fusing the positioning distribution map and pulse characteristics, outputting a global discharge point correction distribution map, identifying a discharge type based on a discharge characteristic spectrum, generating an insulation state assessment result and feeding back the assessment result to a terminal user; and the application provides a high-reliability, high-efficiency and low-cost solution for insulation state control of a distribution network.

The invention discloses an edge analysis early warning method and device for power grid equipment installation data, and relates to the technical field of power grid equipment installation data processing, and the method comprises the steps: collecting multi-source heterogeneous original data, analyzing the data type, and distributing the data to a corresponding preprocessing channel for adaptive preprocessing; and obtaining the current real-time computing power load of each node to perform grading lightweight processing, loading the data analysis model and the early warning rule base according to the type of lightweight data, and performing real-time analysis and risk early warning. Different types of data can be subjected to differentiated self-adaptive cleaning, dynamic lightweight adaptation is performed according to the computing power load, the original features of the data are reserved to the maximum extent, and the real-time performance and accuracy of risk early warning are ensured. The system can be well adapted to multiple link installation construction sites of various power grid devices, and can timely find low-quality data to ensure the stability and reliability of data analysis. The cloud-edge collaborative knowledge update enables the edge computing nodes to be globally and continuously evolved, and the defect of model solidification is overcome.

The invention belongs to the field of intelligent control of high-frequency switching power supplies, and relates to an intelligent high-reliability communication power supply device. The device comprises a novel high-frequency switching power supply acquisition gateway. The novel acquisition gateway for the high-frequency switching power supply comprises a data acquisition module, an edge calculation module, a protocol conversion module, a communication module and a power supply module, according to the technical scheme provided by the invention, aiming at the defects of insufficient data acquisition dimension, lack of edge analysis capability, poor protocol compatibility and insufficient real-time performance of the existing power supply double-path switching system, the reliability of the high-frequency switching power supply for communication is improved through cooperative work of the novel acquisition gateway and the high-reliability double-power-supply automatic switching system.

The invention provides an intelligent visual control recognition system and method for a coal mine collection working face and electronic equipment, and the system comprises coal mining equipment, and also comprises a sensing module which is arranged on the coal mining equipment and is used for collecting video data of the internal working face of a coal mine; the edge analysis module is in signal connection with the sensing module and is used for receiving the video data, analyzing the video data by adopting a preset algorithm model and judging whether an abnormal working condition occurs or not according to the video data; and the control module is in signal connection with the edge analysis module and is used for adaptively adjusting operation parameters of the coal mining equipment according to an analysis result of the edge analysis module when an abnormal working condition occurs so as to perform linkage control on the coal mining equipment. Abnormal conditions in the coal mining process can be found and processed in time, the coal mine operation safety and the production efficiency are improved, and the manual monitoring cost is reduced.

This invention discloses an unmanned security management system for smart construction sites, relating to the field of security management. It includes: a security deployment module for constructing a three-dimensional security deployment network; a deployment management module for setting corresponding edge monitoring nodes according to the constructed three-dimensional security deployment network; a data acquisition module for acquiring security monitoring information; an edge processing module for performing edge analysis on the acquired security monitoring information to obtain node security early warning information at the corresponding edge monitoring nodes; a collaborative processing module for collaboratively processing the security early warning information of each node according to its location within the three-dimensional security deployment network to obtain collaborative feedback information; and a security management module for obtaining feedback monitoring information based on the collaborative feedback information, and performing comprehensive security management based on the security monitoring information and feedback monitoring information to generate security management information. This invention improves the efficiency and accuracy of security management to a certain extent.