600 results about "Alarm trigger" patented technology

The invention relates to the technical field of public safety and intelligent fire protection, in particular to a real-time processing method and system based on fire alarm data, and the method comprises the steps: environment steady state reconstruction: accessing non-fire environment dynamic parameters; presetting thermotechnical static parameters of the building space; constructing a space thermal inertia differential equation; generating an ideal reference baseline; knowledge-driven simulation: receiving the ideal reference baseline; calling a synthesis operator in a preset disaster interference knowledge base; executing dynamic superposition injection; generating a virtual sensor state flow which has physical characteristics and contains environmental background characteristics; homomorphic judgment: executing double difference calculation; obtaining a real residual feature vector; geometric homomorphism verification is executed; outputting fire alarm triggering, interference filtering or fault prompting instructions; according to the method, the problem of false alarm caused by non-stable fluctuation of the environment background in the background technology is solved, and dynamic fusion and scene adaptation of the standard signal model and the real environment background are realized.

The invention relates to the technical field of liquid level detection, in particular to a scrubber sewage tank water level detection system and method based on a liquid level sensor, and the system comprises a sensing data acquisition module, a liquid level signal conversion module, a multi-source numerical value comparison module, a liquid level fluctuation recognition module and an alarm action linkage module. According to the invention, through acquisition of a sensor capacitance change sequence, elimination of abnormal fluctuation, construction of a stable reference curve, enhancement of data immunity, mapping of a liquid level section based on an average capacitance value, accurate identification of liquid level elevation is realized, synchronous comparison of liquid level state information in various communication protocols is realized, and data consistency and reliability are improved. A liquid level fluctuation boundary is accumulatively recognized through trend direction change, a change section is dynamically marked, alarm triggering is based on boundary area and state mark double verification, warning response accuracy and effectiveness are enhanced, and a high-precision and high-stability liquid level detection and early warning system is constructed.

The invention relates to the technical field of medical monitoring, and discloses a novel infusion alarm and an infusion alarm system. The system comprises an infusion state monitoring module, an alarm decision module and an alarm execution module. A multi-source sensing unit of the infusion state monitoring module is distributed in a liquid level detection area, a drip chamber area and a patient contact area of an infusion tube assembly and collects optical refractive index, gravity change and real-time image data, and a data fusion unit performs heterogeneous signal fusion on the data to generate a multi-mode infusion state signal. A state analysis unit of the alarm decision module receives the signal and outputs a liquid level prediction signal and an infusion abnormal risk signal, and an alarm rule engine generates an alarm trigger threshold according to the signal. And a sound-light alarm unit of the alarm execution module responds to the threshold value to start a sound-light alarm sequence, a remote notification unit generates a graded alarm notification based on the abnormal risk signal, and a man-machine interaction interface outputs a comprehensive risk index.

The invention relates to the technical field of electrical safety monitoring, in particular to an electrical operation environment abnormity monitoring and automatic alarm system based on the Internet of Things, which comprises an environment data acquisition module, a fault identification and analysis module, a priority ranking module and an alarm triggering module, wherein the environment data acquisition module is used for acquiring environment data in real time; the fault identification and analysis module is used for identifying abnormal conditions in the working environment of the electrical equipment and automatically judging whether a fault exists or not; the priority ranking module is used for distributing a priority for each fault; the alarm triggering module is used for automatically triggering alarm measures according to the fault priorities output by the priority ranking module; according to the invention, by combining the Internet of Things technology, the machine learning algorithm and the intelligent alarm mechanism, real-time monitoring, accurate fault identification and priority ranking of the electrical working environment are realized, the accuracy and response speed of fault early warning are improved, and the equipment safety is effectively guaranteed.

The invention discloses a hydroelectric generating set fault early warning method, system and equipment based on a knowledge graph and a medium, and belongs to the technical field of hydroelectric generating set fault early warning, and the method comprises the steps: constructing a hydroelectric generating set knowledge graph database, and representing a set structure, operation information and a fault association relationship; collecting real-time operation data of the hydroelectric generating set, and performing data preprocessing; based on the knowledge graph and the collected data, path traversal and node matching are carried out by adopting a multi-modal reasoning mechanism fusing rule reasoning and case reasoning, potential abnormal parameters are identified, and a candidate fault hypothesis set is generated; and when the confidence coefficient of any fault hypothesis exceeds a set threshold value, outputting corresponding fault early warning information. According to the method, a fault hypothesis system is constructed by means of an existing fault propagation path in a knowledge graph and abnormal data captured by real-time monitoring, the confidence coefficient of fault hypothesis is accurately calculated based on the Bayesian network, and timely and accurate fault alarm triggering is achieved.

The invention relates to the technical field of state monitoring, in particular to an energy-saving air compressor running state monitoring system and method, and the system comprises a pressure slope analysis module, a deviation trend judgment module, an error fitting acquisition module, an error trend analysis module and an alarm triggering module. According to the method, a pressure range is calculated by dividing time slices, a slope ratio is generated, a dynamic threshold algorithm is combined to successively compare and capture an offset trend, frequency and current waveform data are synchronously acquired to match a timestamp to calculate a difference value, an error vector is generated in a standardized manner, and a periodic mean value is extracted to verify an incremental relationship to identify progressive anomaly. The cross validation slope ratio and error vector positioning wear or coordination abnormity, the dynamic threshold value is adjusted along with the mean value coefficient at the initial period to reduce fluctuation interference, the timestamp matching enhances the composite fault detection capability, and the range division operation quantifies the matching degree to provide an energy efficiency optimization basis. And a standardized error vector and mean value incremental mechanism improves the prediction precision and reduces false alarm and missing alarm.

The invention discloses a monitoring equipment fault rapid positioning method based on causal diagram reasoning, and the method comprises the following steps: building a thermal diffusion model composed of equipment nodes and links through collecting the topological structure and operation data of a monitoring system; normalizing and fusing the operation deviation information into a node abnormal energy value, and establishing an abnormal energy propagation equation; when an alarm is triggered, collecting an energy change sequence of each node to generate a space-time energy distribution matrix, and reversely solving a thermal diffusion equation to calculate a disturbance source item; performing clustering backtracking on disturbance source items, determining fault root cause nodes, and comprehensively evaluating confidence; and when multi-source abnormity exists, thermal field orthogonal decomposition is executed, and model parameters are adaptively updated according to field results. According to the method, abnormal propagation of monitoring equipment is modeled into a thermal diffusion process, and reverse solution and adaptive calibration are combined, so that rapid, explainable and high-precision positioning of fault root causes in a complex monitoring network is realized.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent traffic-oriented 5G + V2X module and risk early warning terminal and an application method thereof, and the intelligent traffic-oriented 5G + V2X module and risk early warning terminal comprises an equipment end and a cloud platform. The equipment end is mounted on a vehicle and comprises a real-time data acquisition module, a front-end anomaly detection model and a risk alarm; the real-time data acquisition module is responsible for acquiring data related to a vehicle and real-time data of the surrounding environment of the vehicle; the front-end anomaly detection model is deployed at an equipment end; the risk alarm triggers an alarm device when the front-end anomaly detection model detects a potential risk or an abnormal condition; the cloud platform comprises a data extraction module, a data center module and a real-time calculation module; wherein the data extraction is responsible for receiving, extracting and storing data acquired by a vehicle sensor and a camera from an equipment end; the data center stores and manages a large amount of vehicle data; accurate collection of the vehicle body motion state is improved, the position information of the vehicle is more accurate, and the tracking capacity of the system on the vehicle position and the driving track is improved.

The invention relates to the technical field of charging station fire safety management, and discloses an intelligent charging station fire early warning alarm system and method, and the system comprises an intelligent sensing unit, a data transmission network, a data processing and analysis module, and an early warning information issuing module. The alarm system comprises an alarm triggering unit, an alarm information processing unit, an alarm information issuing unit and a linkage control unit. According to the intelligent charging station fire-fighting early warning and alarming method, through an efficient data processing algorithm, an accurate fire identification technology and a flexible early warning and alarming strategy, the early warning accuracy and the alarming timeliness are improved, through simulation experiments and actual case verification, excellent performance is shown, the early warning accuracy is high, the alarming response speed is high, and the intelligent charging station fire-fighting early warning and alarming method is suitable for popularization and application. Moreover, the system is high in stability, integrates an advanced intelligent sensing unit, a data processing and analysis module, an early warning and alarm mechanism and a linkage control module, and achieves the real-time monitoring and intelligent analysis of the environment of the charging station.

The invention relates to the technical field of anomaly monitoring, in particular to a BMC running state monitoring method and system for a microcomputer host, and the method comprises the steps: determining the temperature anomaly degree according to the abnormal change of the CPU temperature after the rotation speed of a radiator influences the CPU temperature and the abnormal rising trend of the CPU temperature; determining the abnormal heat dissipation degree according to the characteristic that the temperature of the CPU is usually reduced after the rotating speed of the radiator is increased; on the basis of the two, the alarm triggering risk is determined by combining the stable condition of the host operation state; determining an alarm response demand degree according to the numerical value abnormity of the CPU temperature at all moments from the occurrence of the overtemperature to the alarm; according to the method, the more accurate dynamic de-jitter time window is obtained by combining the window adjustment coefficient adaptively determined by the alarm triggering risk and the alarm response demand degree, so that the accuracy of alarm detection is higher, and the effect of monitoring the BMC running state of the microcomputer host according to the dynamic de-jitter time window is better.

The invention provides a fault recording data storage method and device of an energy storage system and a storage medium, and relates to the technical field of energy storage, and the method comprises the steps: sequentially caching a plurality of data packets sequentially sent by a battery management system in the energy storage system for each energy storage device in a plurality of energy storage devices into a first internal storage medium; taking the data packet from the first internal storage medium by adopting an analysis task, and analyzing the taken current data packet to obtain equipment information and analysis data corresponding to the current data packet; according to the equipment information, storing the analysis data into a target storage block of the target equipment corresponding to the equipment information in a second internal storage medium; and according to the alarm triggering time of the equipment alarm information, writing multiple pieces of data in a preset time period before and after the alarm triggering time in the storage block of the target equipment in the second internal storage medium into the external storage medium. According to the invention, the integrity and reliability of the fault data of the energy storage system are ensured.

The invention discloses a millimeter-wave radar fusion system and method for electrocardiograph monitoring false alarm recognition, and the method comprises the steps: carrying out the multi-scale time-frequency feature extraction of a millimeter-wave radar signal and an electrocardiograph signal, obtaining a radar feature sequence and an electrocardiograph feature sequence, carrying out the time alignment of the radar feature sequence and the electrocardiograph feature sequence, and inputting a multi-modal fusion architecture; outputting cross-modal joint embedding features based on the multi-modal fusion architecture; a cross-modal consistency judgment model obtained based on self-supervised contrast learning optimization training is constructed, whether a cross-modal feature inconsistent state exists in the joint embedded features or not is judged, and if yes, it is judged that a potential false alarm event exists; and based on the potential false alarm event, extracting sequence feature fragments before and after alarm triggering in the joint embedded feature, inputting the sequence feature fragments into an anomaly classification network, outputting a final judgment result about whether false alarm is formed, and when the final judgment result is false alarm, generating an alarm adjustment instruction and sending the alarm adjustment instruction to a monitoring terminal.

The invention discloses an electrical equipment real-time alarm method based on multi-sensor data fusion, which belongs to the technical field of sensor control and comprises the following steps: acquiring multi-source sensor data of electrical equipment, identifying an initial abnormal signal and combining with a preset fault-sensor mapping table; activating a corresponding target sensor group to collect data, performing physical parameter calibration, generating a standardized sensing data set, executing space-time correlation feature extraction, and generating a cross-sensor feature vector; and processing the cross-sensor feature vector based on a conditional probability tree model, outputting a probability value set of each fault type, comparing the probability value set with a probability threshold, generating an alarm trigger signal according to a comparison result, verifying the alarm trigger signal, and generating a final alarm instruction. The fault-oriented dynamic sensor activation mechanism, the physical parameter calibration technology, the fault decision model based on the conditional probability tree and the reverse verification operation are adopted, accurate fault recognition and low-false-alarm-rate alarm can be achieved, and meanwhile the system energy efficiency ratio and the long-term diagnosis stability are improved.

The invention discloses a cross-platform development board communication fault feedback method and device and a storage medium, and relates to the technical field of communication, after main and sub development boards are initialized, a communication state machine is established, a heartbeat timer is started, heartbeat packets are periodically received and sent, an overtime threshold value is dynamically set in an idle state, and error feedback is triggered if no heartbeat packet is received in an overtime state. The connection state periodically detects communication quality, a reconnection mechanism is triggered if continuous packet loss exceeds a threshold value, an error report is generated and reported when reconnection failure reaches an upper limit, error feedback comprises data packet sending, log recording, alarm triggering and the like, and a reconnection request comprises an identifier, a timestamp and a historical communication index; according to the method, communication interruption caused by misjudgment is reduced, and the communication stability and reliability are improved.

The invention provides a power industry fire alarm control system which is provided with an environment monitoring module, a data processing module, a control module, a communication module and an alarm triggering module. Then the data is transmitted into a data processing module to be processed, and normalization processing, multi-parameter weighted fusion, fire risk index calculation and anomaly detection of the data are carried out; after power fire information is detected, related equipment is controlled through the control module, and the effect of extinguishing fire and protecting the equipment is achieved; the communication module transmits the system state and the alarm information to an external monitoring system or a remote control center; the alarm triggering module sends out an alarm signal to remind an operator on duty in time; according to the system, various environment monitoring sensors are integrated, and a weighted fusion algorithm, an anomaly detection mechanism and an automatic linkage control technology are adopted, so that efficient fire detection and emergency response functions are realized.

The utility model belongs to the technical field of automobile testing, and provides a quick tripping device applied to a safe evacuation system, which comprises a shell, an electromagnet, a shifting block, a push-pull rod, a first spring, a locking piece and a pull ring, the shifting block is rotationally connected to the shell; the push-pull rod is arranged in a sliding mode in the first direction and is provided with a farthest stroke point and a nearest stroke point relative to the pull ring. The first spring is arranged in the first direction and can make contact with the locking piece. The locking piece is located in the shell and connected with the push-pull rod, and the locking piece has a clamping form and a releasing form along with movement of the push-pull rod; electric tripping is achieved by means of the electromagnet, locking and tripping states of the pull ring are achieved through a mechanical structure, linkage of all parts is convenient and rapid, corresponding alarm triggering signals are achieved, and the structural safety is good.

The invention discloses a driver state remote monitoring collaborative alarm platform and method based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: carrying out the channel information entropy analysis of collected state data, adaptively cutting off a low-information-amount channel, carrying out the nonlinear feature mapping to obtain a mapping feature sequence, constructing a space-time coupling information tensor, and carrying out the self-adaptive cutting of a low-information-amount channel; the method comprises the following steps: calculating a multi-mode cooperative surge factor, executing tensor decomposition to obtain a decomposition factor, reconstructing an estimation tensor and calculating a reconstruction error, calculating a multi-mode entropy surge factor, generating an abnormal score, counting and generating a dynamic threshold according to the abnormal score in a sliding window, and judging a real-time score to generate an alarm trigger signal. Through adaptive dimensionality reduction, hybrid mapping, dynamic modulation CP decomposition and adaptive reconstruction error evaluation based on channel information entropy, real-time efficient processing and deep multi-modal fusion of multi-source data are realized, noise and redundancy are effectively eliminated, abnormal features are amplified, space-time coordination is accurately described, and dynamic adaptive anomaly judgment is realized.

The invention discloses a single-axis mechanical accelerometer and a measuring method, and relates to the technical field of accelerometer measuring equipment.The single-axis mechanical accelerometer comprises a base and a dome shell installed on the base, a first cavity is formed in the base, and an I-shaped balancing weight capable of axially sliding is installed in the first cavity; a first spring is mounted between the I-shaped balancing weight and the cavity bottom of the first cavity; a conical sleeve capable of sliding in the radial direction is installed in the dome shell, a transverse shaft is inserted into the conical sleeve in a penetrating mode, the two ends of the transverse shaft are fixedly connected to the inner wall of the dome shell, the transverse shaft is sleeved with a second spring, one end of the second spring is fixedly connected to the transverse shaft, and the other end of the second spring is fixedly connected with the conical sleeve. An alarm triggering module is installed on the dome shell, and through the synergistic effect of an I-shaped balancing weight-spring inertia system and a conical sleeve triggering mechanism, the acceleration detection and alarm functions of a pure mechanical structure are achieved.

The invention discloses a contact type alarm output device for a mechanical accelerometer, and relates to the technical field of accelerometers, the contact type alarm output device comprises a dome shell, and the dome shell is provided with a first cavity; the first contact, the second contact, the first alarm output lead and the second alarm output lead are inserted into the dome shell in a penetrating mode, the first alarm output lead is connected to the end, inserted into the outside of the dome shell, of the first contact, and the second alarm output lead is connected to the end, inserted into the outside of the dome shell, of the second contact. The second alarm output lead is connected to the end, penetrating into the outer portion of the dome shell, of the second contact, one end of the metal spring piece is fixedly connected to the first contact, the other end of the metal spring piece is in point contact with the second contact, and the metal spring piece and the second contact are in point contact design. And in combination with mechanical pushing of the conical sleeve, tight contact and reliable conduction during alarm triggering are ensured, and mistaken contact or poor contact caused by vibration or impact is avoided.

The invention relates to the technical field of intelligent security and protection, and discloses an intelligent security and protection alarm method and system based on an intelligent park. The method comprises the following steps: acquiring original security and protection data by using sensing equipment arranged in a smart park; performing standardization and redundancy elimination processing on the original security and protection data, dividing safety areas of the park through a pattern recognition algorithm, calculating a load intensity index of each area, and generating a security and protection situation summary; according to the security situation summary, a heuristic search algorithm is adopted to determine alarm trigger parameter initial values including detection sensitivity and alarm intervals, and a basic alarm parameter group is generated; performing nonlinear correction on the basic alarm parameter group, and outputting an environment perception alarm parameter group; training the historical security data set by adopting a neural network model, and analyzing the interaction between the load and the threat to obtain a threat diffusion model; and comparing the threat diffusion model with the real-time security data points, performing difference measurement, reconstructing alarm trigger logic according to a measurement result, and completing autonomous regulation and control of security and protection of the smart park.

The invention discloses an electrocardiogram waveform real-time anomaly detection and early warning evaluation method and system, and the method comprises the following steps: S1, collecting the signal data of an electrocardiogram, and carrying out the preprocessing; s2, inputting the electrocardiogram segment sequence into an improved convolutional neural network, and performing feature extraction and aggregation; s3, inputting the fusion feature sequence into a graph attention neural network, and extracting a relation between waveform local dependence and fragment state transition; s4, inputting the global feature map into an anomaly evaluation network, and outputting an anomaly judgment result and a confidence score sequence; s5, generating a risk level tag sequence in combination with the individual parameters; s6, inputting the risk level label sequence into an early warning decision network, and generating a multi-level response label and an alarm trigger signal; and S7, controlling an early warning interface to execute alarm, and synchronously writing related data. According to the invention, real-time anomaly detection and multi-stage early warning response of the electrocardiogram waveform are realized, and the timeliness and reliability of abnormal heart rhythm recognition are improved.

Systems, methods and apparatus for automatic alarm management in a radio-frequency (RF) environment are disclosed. An apparatus calculates a power distribution by frequency of the RF environment in real time or near real time, including a first derivative and a second derivative of FFT data of the RF environment. The apparatus then creates a baseline based on the power distribution by frequency of the RF environment in a period of time, identifies at least one alarm situation based on a multiplicity of alarm triggering conditions by comparing the power distribution in real time or near real time to the baseline of the RF environment, identifies at least one signal based on the first derivative and the second derivative of FFT data in the at least one alarm situation, and sends at least one alarm comprising details of the at least one signal identified in the at least one alarm situation.

The invention relates to the technical field of medical instruments, and discloses an old people physiological information emergency alarm system based on big data, which comprises a data acquisition module, a data processing module, a signal data analysis module, a behavior characteristic analysis module, a physiological characteristic analysis module and an alarm module. The problems of incomplete data acquisition, insufficient signal interference correction capability, lagging behavior characteristic and physiological characteristic analysis, frequent false alarm and missing alarm of an alarm system and the like in the traditional technology are solved. Compared with the prior art, the system has the advantages that the comprehensiveness of data acquisition, the depth of analysis, the accuracy of alarm triggering and the like are remarkably improved. Particularly, by introducing a multi-dimensional screening mechanism of a data correction coefficient SQI and a physiological behavior judgment coefficient XWF, the detection capability of the system on abnormal conditions is optimized, the reliability and efficiency of health monitoring are remarkably improved, and therefore a powerful guarantee is provided for timely intervention of sudden health problems of old people.

The invention belongs to the technical field of foreign matter detection and alarm, and discloses a vehicle-mounted multi-sensor fusion runway FOD detection and alarm device. Comprising a data acquisition module for acquiring radar data, visual data and infrared data, and performing space-time synchronization and coordinate calibration to obtain synchronous data; the feature analysis module is used for analyzing the synchronous data to obtain radar features, visual features and infrared features; the analysis and complementation module is used for detecting the radar features, the visual features and the infrared features to obtain combined features, and performing shielding detection and feature complementation on the combined features to obtain complemented features; constructing an FOD dynamic feature tree; the positioning alarm module is used for analyzing the FOD dynamic feature tree and making an FOD alarm decision according to an alarm triggering condition; according to the method, accurate detection and reliable alarm of the runway FOD are realized, the accuracy, robustness and practicability of FOD detection in a complex scene are remarkably improved, and the runway FOD safety management and control requirements in a vehicle-mounted scene are effectively met.

The invention relates to the technical field of driver testing, in particular to a testing method, system and device of a multi-axis integrated servo driver and a medium. The method comprises the following steps: firstly, synchronously collecting communication information such as an EtherCAT bus state and a connection state, and operating parameters such as a band-type brake signal, torque feedback and an IO state, and constructing a complete state data chain; then analyzing the collected data in real time to obtain a function test result; carrying out abnormity judgment and alarm triggering based on a test result; finally, the test process is intelligently adjusted according to the alarm information, the accuracy of fault diagnosis is improved through cross analysis of the information, meanwhile, self-adaptive adjustment of the detection process is achieved, and the test efficiency and reliability are remarkably improved.

The invention relates to the technical field of medical monitoring, and discloses an intelligent monitoring device for a blood return end of hemodialysis, the device is in communication connection with a dialyzer for hemodialysis, and the device comprises an alarm trigger module, a temperature compensation module and a screen display module. According to the application, the alarm triggering module accurately monitors the blood return temperature, the blood return flow velocity and the induced blood flow velocity, captures abnormity in real time and triggers alarm; the temperature compensation module not only guarantees life safety of the patient, but also improves temperature control adaptability; the screen display module displays core parameters and trend curves in real time, space limitation is broken through, and medical workers are assisted in remote quick response; the portability of the device is realized by the micro clamping and fixing module; the clinical flexibility is enhanced through threshold adjustment; the power-on self-test further ensures the operation safety of the equipment; therefore, full-process optimization from data monitoring, intelligent control to remote monitoring is realized, and accurate, safe and personalized dialysis support is provided for patients.

The invention relates to the technical field of safety monitoring, and provides a safety monitoring method and device based on artificial intelligence, monitoring equipment and a medium. According to the method and the device, abnormal behavior detection is performed on the monitoring video containing the monitoring object through the AI model, so that the video can be analyzed without interruption and omission for 24 hours, and the possibility of human negligence and misjudgment is reduced. When the abnormal behavior is detected, the video clip related to the abnormal behavior is extracted from the monitoring video and sent to the monitoring manager for manual verification, so that the amount of video needing to be checked by the monitoring manager is reduced, the working efficiency is improved, the monitoring manager is ensured to directly pay attention to the specific situation of the abnormal behavior, and the user experience is improved. And corresponding decisions can be made quickly. And when the verification feedback mark of the monitoring manager is received and the verification feedback mark is matched with the preset alarm triggering mark, the alarm system is triggered, so that the conditions of false alarm and missing alarm are avoided, and the effectiveness and credibility of the alarm system are improved.

The invention relates to the field of identification and detection, in particular to a fall identification method based on millimeter wave radar point cloud data and a height threshold, which comprises the following steps of: 1, acquiring radar point cloud data; 2, extracting accurate point cloud data from target point information; 3, calculating three-dimensional point cloud data through an algorithm; 4, height information extraction and dynamic height threshold value fitting; 5, tumble identification is carried out; according to the method, the high-precision perception capability of the millimeter wave radar and the dynamic fitting model of the height threshold are combined, the accuracy of fall detection can be effectively improved, and the method has high environmental adaptability and privacy protection characteristics and is suitable for families, nursing homes, hospitals and other scenes.

The invention relates to the technical field of visual information integration supervision, and particularly discloses a building security-oriented visual information integration supervision system, which comprises a data acquisition module, an exception filtering module, a risk assessment engine module, an alarm triggering module, a visual rendering module, a response execution module and a closed-loop feedback module. According to the method, accurate risk management and control are realized through multi-source data fusion and dynamic analysis, data such as videos, heat values and gas concentrations are acquired, a clean risk feature packet is generated through abnormal filtering, a thermal diffusion trend and an outlet blockage risk degree are predicted by using a thermal conduction model and a building model, and a dynamic safety score is generated through normalization processing. The system automatically triggers multi-level alarms, renders an interactive monitoring graph in combination with a three-dimensional model, optimizes an evacuation path and generates a rescue report, and finally drives equipment response through closed-loop feedback; the security response efficiency and the resource allocation accuracy are improved, and the accident risk of high-rise and closed buildings is reduced.